text:
Next Article in Journal
Experimental Research on Excitation Condition and Performance of Airflow-Induced Acoustic Piezoelectric Generator
Previous Article in Journal
Feasibility Study for a Chemical Process Particle Size Characterization System for Explosive Environments Using Low Laser Power
Previous Article in Special Issue
Recent Advances of Wearable Antennas in Materials, Fabrication Methods, Designs, and Their Applications: State-of-the-Art
10.3390/mi11100912
Submit to this Journal Review for this Journal Edit a Special Issue
► ▼ Article Menu
Subscribe SciFeed
Related Info Links
PubMed/Medline
More by Authors Links
on DOAJ
Khan, M. Bilal
Zhang, Z.
Li, L.
Zhao, W.
Hababi, M. Ali Mohammed Al
Yang, X.
Abbasi, Q. H.
on PubMed
/ajax/scifeed/subscribe
Citations -
Altmetric share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
Endorse textsms
JSmol Viewer
Order Article Reprints
Arial Georgia Verdana
  
Column Width:
  
Open AccessReview
A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19
Muhammad Bilal Khan
Zhiya Zhang
Lin Li
3,*,
Mohammed Ali Mohammed Al Hababi
Xiaodong Yang
1,* and
Qammer H. Abbasi
School of Electronic Engineering, Xidian University, Xi'an 710071, China
Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Islamabad 43600, Pakistan
Key Laboratory of Quantum Optics, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
School of Mechano-Electronic Engineering, Xidian University, Xi'an 710071, China
School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
Authors to whom correspondence should be addressed.
Micromachines 2020, 11(10), 912; https://doi.org/10.3390/mi11100912
Received: 30 August 2020 / Revised: 27 September 2020 / Accepted: 29 September 2020 / Published: 30 September 2020
(This article belongs to the Special Issue Future Wearable and Implants)
Download PDF with Cover
Browse Figures
Versions Notes
The rapid spread of the novel coronavirus disease, COVID-19, and its resulting situation has garnered much effort to contain the virus through scientific research. The tragedy has not yet fully run its course, but it is already clear that the crisis is thoroughly global, and science is at the forefront in the fight against the virus. This includes medical professionals trying to cure the sick at risk to their own health; public health management tracking the virus and guardedly calling on such measures as social distancing to curb its spread; and researchers now engaged in the development of diagnostics, monitoring methods, treatments and vaccines. Recent advances in non-contact sensing to improve health care is the motivation of this study in order to contribute to the containment of the COVID-19 outbreak. The objective of this study is to articulate an innovative solution for early diagnosis of COVID-19 symptoms such as abnormal breathing rate, coughing and other vital health problems. To obtain an effective and feasible solution from existing platforms, this study identifies the existing methods used for human activity and health monitoring in a non-contact manner. This systematic review presents the data collection technology, data preprocessing, data preparation, features extraction, classification algorithms and performance achieved by the various non-contact sensing platforms. This study proposes a non-contact sensing platform for the early diagnosis of COVID-19 symptoms and monitoring of the human activities and health during the isolation or quarantine period. Finally, we highlight challenges in developing non-contact sensing platforms to effectively control the COVID-19 situation.
COVID-19; CSI; non-contact; SDR; Wi-Fi
The pandemic of COVID-19 is exponentially spreading all over the world. Due to this exponential increase, many people have been affected or have died, and as a result the entire world is quarantined from each other. As the outbreak continues to evolve, every country's government is considering options to prevent the spread of the virus to new places by stopping human movement in places where the disease that causes COVID-19 is already circulating [1]. Self-quarantine is the only option to make one's own and others' lives safe. The quarantine of a person is the limit of activities or the separation of persons who are not actually ill but who may be exposed to disease or an infectious agent, whereas isolation is the separation of infected or ill persons from others to prevent the spread of infection or contamination. Country authorities should properly communicate to people before implementation of quarantine and take such measures to improve compliance and reduce panic. They must provide people with clear, consistent and up-to-date guidelines and with reliable information about quarantine measures [2]. It is essential to have constructive engagement with communities in case quarantine measures are to be taken. Persons who are quarantined need to be provided with financial and social–psychosocial support, health care and basic needs, including water, food and other life necessities. The requirements of the vulnerable public should be prioritized. Geographic, cultural and economic elements affect the efficiency of quarantine. Prompt judgment of the local context should assess both the potential barriers and drivers of success to quarantine, and they should be used to notify plans for the best applicable and culturally-known measures. In the situation of the present COVID-19 outbreak, the worldwide containment plan consists of rapid laboratory tests to identify confirmed cases and control them, either in a hospital or at home during the isolation period. Any individual in quarantine who feels a febrile illness or respiratory symptoms at any point should be treated and registered as a suspected case of COVID-19 [1]. However, quarantine is the only existing solution to contain the spread of the virus and to maintain the necessities of life. In this regard, scientists, doctors, engineers and many different communities are doing their best to find promising solutions.
In this study, the focus is on the development of a platform that can diagnose early symptoms of COVID-19 and monitor human activities and health during quarantine and isolation periods. Although wearable sensors and camera-based technology are mature solutions for monitoring of human activities and health [3], wearable sensors have direct contact with the human body, so they may become a source of spreading virus; on the other hand, camera-based technology has no direct contact with the human body but still has issues with individual privacy and the monitoring of blind spots [4,5]. Due to these technical and social issues, non-contact sensing platform development is proposed to avoid any kind of contact in monitoring during the COVID-19 pandemic. Recently, extensive research on activity recognition and classification (ARC) of human activities in the field of health care has resulted in promising new solutions [6]. Specifically, device-free ARC-based platforms are becoming popular. A software-defined radio (SDR) technology-based platform was designed for the detection of human activity. This platform can be used for multiple health applications due to flexibility and scalability of the software-defined hardware [7]. The device-free sensing approach is becoming very popular because patients are not required to carry any device, but instead the wireless channel is tagged with devices to capture the required information [8]. Wi-Fi-based devices have grown very rapidly because they are easy to deploy and are cost effective. Recently, wireless channel state information (WCSI) captured by Wi-Fi devices has been widely used for different sensing purposes [9,10,11,12].
Advances in wireless sensing technologies have the potential to reduce the health care services load from hospital to home, thereby securing hospital facilities. The deployment of wireless sensing health monitoring technology is a promising and feasible solution to handle the COVID-19 pandemic. In this review, a comprehensive study of various platforms used for early diagnosis of vital signs, human activity monitoring and health is presented. This study proposes a non-contact platform for the early diagnosis of COVID-19 symptoms and the monitoring of human activities and health during the quarantine or isolation period. The main goal is to identify non-contact sensing technologies used for monitoring human daily activities like sitting, standing, walking, sleeping and eating, and health monitoring, such as respiration rate, heartbeat, fall, sleep disorder and a balanced diet. This study proposes a solution using the existing literature to develop a platform to diagnose early symptoms of COVID-19 and monitor human activities and health during the quarantine and isolation period in a non-contact manner. Although COVID-19 affects different people in different ways, most infected people will develop mild to moderate symptoms including cough, fever and shortness of breath [2]. The review presents the methods of data collection using non-contact sensing technology, the human activity and health condition classification approach and performance achieved by the existing platforms. We categorize the studies into monitoring of human activities and health conditions. Furthermore, the review presents advantages and limitations. Finally, we summarize and explain some challenges to open research problems that require further investigation and improvements.
The following are the contributions of the comprehensive study:
This study provides a road map in developing a COVID-19 pandemic platform for containing the virus.
Systematically review the non-contact sensing platforms used for human activity and health monitoring.
Propose a non-contact sensing platform for the early diagnosis of COVID-19 symptoms and the monitoring of human activities and health during the isolation or quarantine period.
Highlight the challenges, testing environment, performance and optimal solutions to work on deployment.
The rest of paper is organized as follows: Section 2 includes a literature review of the COVID-19 pandemic, the existing non-contact wireless sensing platforms and technology exploited, the monitoring of human activities and health, and the classification approach and accuracy achieved. In Section 3, the proposed platform is described for the early diagnosis of COVID-19 symptoms and monitoring of human activities and health during the isolation or quarantine period. In Section 4, the experimental setup based on both commercial and specialized hardware is presented. In Section 5, the advantages of developing a non-contact WCSI sensing platform for containing COVID-19 are explained. In Section 6, the challenges faced in developing a non-contact sensing platform are discussed. In Section 7, future recommendations and possible solutions are discussed. In Section 8, conclusions on the non-contact sensing platform development for containing the COVID-19 are made. This study used a list of abbreviations, as defined in Table 1.
2. Literature Review
In this section, we present a summary on the origination, spreading mechanisms, symptoms and prevention methods of COVID-19. Then follows a systematic review of non-contact sensing platforms for human activities and health monitoring. This review identifies reliable and intelligent existing related work to propose a new platform for the early diagnosis of COVID-19 symptoms and monitoring of human activities and health to protect human life.
2.1. Covid-19 Summary
COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The first case was reported in December 2019, in the city of Wuhan, in Hubei Province, China. Since then, COVID-19 has spread like a tsunami around the world and is now present in 213 countries and independent territories. According to the WHO, viral infections initiated by various coronaviruses continue to develop and pose a serious public health problem [13,14,15]. Distinctive features of the virus include its highly contagious nature and relatively long (1–14 days) development period. During this time, a person can become infected by the virus and show no symptoms. Therefore, people who have the disease can act as silent carriers of the virus without realizing it and contribute to a high number of basic reproductions of the COVID-19 virus. To date, there is no specific vaccine or treatment for infection, and management protocols focus on containing disease development. Most COVID-19 cases have exhibited clinical features such as fever, cough and fatigue. Some patients had symptoms such as headache, sore throat and shortness of breath, while symptoms such as runny nose, diarrhea, aches and pains were very rare, as shown in Table 2. While most COVID-19 patients develop mild to moderate disease, a few patients have been diagnosed with a severe (13.8%) and critical (6.1%) health condition [16]. According to the United States Centers for Disease Control and Prevention (CDC), people at the greatest risk of disease from COVID-19 are older adults (those over 60 years of age) and those with pre-existing conditions such as high blood pressure, asthma, diabetes, cardiovascular disease and those taking immunosuppressing therapy [17].
COVID-19 is categorized by a specific dysfunction in respiratory physiological processes involving the other parts of the lower respiratory tract and diaphragm, thereby affecting respiratory patterns during inhalation and exhalation from the lungs [18]. In speech initiation, at the time of exhalation, air from the lungs travels from other basic vocal subsystems, namely the larynx and trachea and the vocal canal into the oral, pharyngeal and nasal cavities. The way we breathe during speech, containing the speed and length of exhalation (depending on the number of words in a sentence or phrase), and its intensity and variability, greatly affect the quality of our voice. In addition, the respiratory system is primarily highly coordinated with these laryngeal-based subsystems [19,20]. Similarly, laryngeal activity is well linked to articulation in the oral and nasal cavities [21]. Although their effects and coordination of speech subsystems are perceptibly apparent with an inflammatory condition, these changes may be mild in the asymptomatic stages of a disease at baseline or recovery. Speech subsystems and coordination are assumed to be affected by COVID-19. In addition to the respiratory involvement by COVID-19, the current pandemic shows evidence that neurological involvement may occur with COVID-19. Headache and dizziness remain the most common symptoms; however, symptoms due to loss of muscle control and loss of proprioception have recently been reported due to transient neuromuscular disorder [22,23], as well as loss of smell and taste [24,25]. Given the physiological disorder to respiratory functions and evidence of this increased neurological issue due to COVID-19, biomarkers of COVID-19 derived from vocal subsystem coordination measurements are the most significant in the asymptomatic stage [26].
Although there are several studies in the direction of COVID-19′s pathophysiological properties, its propagation mechanism remains somewhat indefinable. While the initial COVID-19 cases were associated with the direct exposure of individuals to infected animals, the rapid outbreak of the disease has shifted the focus of the research to human-to-human via direct or other surface transmission. An analysis of around 75,465 cases of COVID-19 in China has revealed that the COVID-19 virus is primarily transmitted between people from the spread of respiratory droplets through coughing and sneezing [27]. These respiratory droplets have the potential to travel a distance of up to 1.8 m (6 feet). Therefore, any person in close contact with an infected person is at risk of being exposed to the respiratory droplets, and by extension, the virus. Although symptomatic people have been identified to be the primary source of SARS-CoV-2 transmission, there is also a possibility of transmission via asymptomatic people. Direct and indirect contact with infected surfaces have been identified as other potential causes of COVID-19 transmission. Evidence suggests that the virus can survive on plastic and steel surfaces. Researchers have revealed that COVID-19 is spread by contact. Therefore, it is recommended to minimize human-to-human contact for the safety of human society.
2.2. Human Activity Monitoring
Human activity monitoring plays an important role in human health. In the COVID-19 pandemic situation, it is essential to monitor human activities in terms of non-contact to stop the spread of the virus. Various non-contact human activity-sensing technologies, methods and performances achieved by existing platforms were investigated for the development of the COVID-19 platform. Device-free detection is a valuable technology for the detection of moving bodies in the operational region without the wearing of any device. The device-free passive detection of moving humans with dynamic speed (PADS) scheme extracts CSI with both types of information (amplitude and phase) and exploits space diversity across multi-antennas in multiple input multiple output (MIMO) systems. The prototype PADS uses commercial Wi-Fi devices to extract shape sensitive metrics for accuracy and robust target detection [28]. An active device-free system uses SDR to exploit activity recognition of a person standing, walking, crawling or lying and/or an empty environment [29]. Since wireless signals are good reflectors of human bodies, activities can be recognized by monitoring received Wi-Fi signals characteristics; CARM proposes a human activity recognition and monitoring system by extracting CSI and was implemented on commercial Wi-Fi devices [30]. A through the wall (TTW) presence detection system for both stationary and moving persons uses Wi-Fi signals with a single Wi-Fi access point (AP). This system considers an empty environment with one stationary human or a human moving in the room; the channel frequency response (CFR) changes over time carry significant information for monitoring activities [31]. Device free solutions based on radio signals (Wi-Fi) available in the home, particular 802.11 standard, have been considered. Fine-grained analysis based on available CSI have been proposed to detect human activities [32]. Human body motions were detected in a quasi-real-time environment using non-contact devices. Patterns of CSI present unique changes caused by body motions to identify particular human activities.
SDR technology has been exploited to extract a dataset that contains radio wave signals patterns [33]. Human activity recognition (HAR) using ultra-wide band (UWB) technology is very effective to investigate the feasibility of device-free activity recognition [34]. Hand gesture recognition is one of the issues in human–computer interactions. Non-contact Wi-Fi-based gesture recognition systems (Wi-GeRs) detect hand motions by capturing the changes in the CSI using Wi-Fi signals. A public Wi-Fi router is used for the detection of hand motions [35]. Non-contact sensing has attained a lot of attraction due to the availability of Wi-Fi signals in homes, offices, shopping malls, airports, etc. The commercial Wi-Fi infrastructure proposed a training-free human vitality sensing platform named Wi-Vit. This platform can capture real-time human motion speed information without the offline training or calibration that involves human effort. The feasibility study of the platform revealed that it can monitor long-term activities of daily living in practice for various applications [36]. Eating is an essential activity in human daily life. In this regard, a device-free system for the monitoring of eating uses Wi-Fi built-in devices (e.g., laptop or smartphone). This system automatically monitors human eating activities by extracting the fine-grained CSI from Wi-Fi signals of eating motions and by detecting swallowing and chewing. It can differentiate non-eating from eating activities and further classifies eating motions with different utensils. Eating monitoring is essential to understand eating behaviors, and it is useful for estimating a balanced diet [37]. The Wi-See system uses Wi-Fi signals for gesture recognition, since wireless signals can traverse TTW and do not require line-of-sight (LOS) from source to destination. The system uses wireless resources to enable entire-home gesture recognition. Wi-See was evaluated in a two-bedroom apartment and an office environment using SDR technology [38]. Wi-Hear uses Wi-Fi signals to "hear" human speech without installing any devices. This system introduces mouth motion profile (MMP) to solve micro movement detection problems that leverage wavelet packet transformation and partial multipath effects. It can "hear" human speech within the radio range, and it can simultaneously "hear" multiple human speech by exploiting MIMO technology. It was implemented on both commercial Wi-Fi infrastructure and the SDR platform [39].
An ambient radar sensor was proposed to recognize human activities in indoor environments. A radar uses 7.8 GHz frequency to capture the fine dynamics of human activities while emitting 16 pulse signals every second. This approach also includes a method to separate a collection of numerous activities into individuals [40]—the concept of domain gap (DG)—and further contains a domain independent (DI) feature, which is a promising solution to eliminate DG and achieve gesture recognition accuracy [41]. The Bumble-Bee radar captures micro-Doppler signatures for indoor human activity recognition. It can discriminate between human activities even under variable conditions [42]. Occupant activity recognition (OAR) is very important for building management systems (BMS) to give comfortable environments for occupants. The Wi-OAR system uses Wi-Fi signals to provide user-centric services and are energy-efficient in smart offices. This system presents a fast and robust target component separation (FRTCS) algorithm for measuring both high accuracy and time efficiency. A pair of commercial Wi-Fi devices was used for developing a prototyped Wi-OAR system in diverse office environments [43]. A public dataset by ten volunteers with sixteen different activities in indoor environments used Wi-Fi signals to develop the Wi-AR system. The aim of the system is to reduce the cost of collected signal data for researchers in a convenient manner and improve the performance in different domains [44]. Wi-Motion uses the amplitude and phase information extracted from the CSI sequence to build the classifiers. This system can recognize six different human activities [45]. A device-free, non-wearable, privacy-preserving occupancy detection system uses Wi-Fi imaging for future smart buildings. This system was developed using an off-the-shelf commercial Wi-Fi router, omnidirectional antennas and a network interface card (NIC) for imminent body-centric communication [46]. A low cost, non-intrusive and minimal low-power radar-based sensing system that uses a novel approach for human activity recognition in the home was developed that investigates fifteen different challenging activities performed inside the kitchen [47].
HAR uses deep learning (DL) networks with enhanced CSI to develop a CSI feature enhancement scheme (CFES). It includes two modules of background correlation feature enhancement and reduction for preprocessing the data input to the DLN [48]. At present, we are entering into the era of the Internet of Things (IoT), where it will be convenient to find APs at any location. The presence of a human body between two APs uses Wi-Fi signal waveforms to extract CSI. Machine learning (ML) uses CSI data to recognize and predict human motion [49]. With the popularization and development of Wi-Fi technology, it has become a benefit of human daily life to use mobile devices for monitoring daily activities [50]. Sleep monitoring is a very important human activity because it plays a key role in human health. Sleep-Guardian, a radio frequency (RF)-based healthcare system, combines signal processing, edge computing and ML [51]. Extensive running is life-threatening if it is not monitored properly. Wi-Run, a non-invasive step estimation, is a complete model-based system that intelligently estimates steps using commercial Wi-Fi devices. Wi-Run consisted of two models. The first model is the single runner CSI-based step estimation, which measures the relationship between single runner running and CSI dynamics in the activity area. The second model is the multi-runner CSI-based step estimation that quantifies the relationship between each runner's running and CSI dynamics in the activity area [52]. Investigating spatial diversity in Wi-Fi signal-based HAR identifies the dead zones and their important dominant factors. A Wi-Fi signal-based spatial diversity aware non-contact activity recognition system (Wi-SDAR) was introduced. It overshadows the dead zones with only one physical Wi-Fi sender and receiver, which is fully compatible with commercial off-the-shelf Wi-Fi devices [53]. HAR uses radar as a sensor having unique characteristics such as contactless sensing and privacy protection. DL methods for activity recognition use radar to exploit human motion information [54].
Table 3 summarizes the non-contact sensing technologies, detection and monitoring, classification methods and accuracy achieved by existing research platform. Human activity classification and monitoring review reveals that non-contact sensing exploits WCSI to study human activities such as sitting, standing, walking, running, eating and sleeping. Wi-Fi sensing using commercial hardware is widely used because it is an inexpensive and easily available solution. Human activities have been monitored and classified in existing research by ML and DL algorithms having accuracies over 90% [28,30,31,32,33,34,35,36,37,38,39,41,42,43,44,45,46,47,48,49,50,51,52,53]. The average accuracy of promising non-contact technologies for monitoring human activities is shown in Figure 1.
2.3. Symptom Diagnosis and Health Monitoring
Regular health monitoring can detect potential health issues before they become a problem. In the COVID-19 pandemic, it is essential to diagnose early symptoms and monitor health conditions in a non-contact manner to stop the spread of the virus. Various non-contact sensing studies for monitoring health were investigated for the development of a COVID-19 platform. Wireless sensing was used to detect asthma attacks based on WCSI where Doppler effects were manifested [55]. The wireless sensing-based healthcare facility utilized 5 G C-band technology, which improved the efficiency of detecting the fall and body motions with a wide spectrum range and maximum capacity. The system works at 4.8 GHz frequency for capturing the WCSI of post-surgical falls and other important activities of humans. The low cost solution includes an RF signal generator, a NIC, and a desktop PC along with omni-directional antennas. This system is feasible and reliable for detecting post-surgical falls with high accuracy [56]. Huntington's disease (HD) is a genetic disorder that cannot be cured easily. The quality of life of patients becomes more serious as the disease quickly progresses. It is essential to examine patients timely and effectively. A microwave sensing platform (MSP) was developed for continuous monitoring of HD patients in a non-contact manner. This platform also resolved patient inconvenience and privacy issues [57].
Parkinsonian gait is the most devastating symptom of Parkinson's disease (PD), which has a more negative impact on quality of life than other PD symptoms. Wireless sensing technology is used for the detection of Parkinsonian gait using S-band for classification of normal walking and abnormal gait. Additionally, the early diagnosis of shaking palsy (SP) symptoms in a non-contact manner was also achieved [58]. Patients suffering from dementia show signs of wandering behavior due to memory loss or boredom. Dementia patients are exposed to serious injuries from falls if they are not continuously monitored. A wireless sensing platform was designed and evaluated the wandering behavior of patients suffering from dementia in an indoor environment [59]. Passive Wi-Fi sensing extracts the two-dimensional phase to monitor three health essentials, includes breathing rate, tremor and falls. The signal processing of the cross-ambiguity function (CAF) and various features are extracted from the signal [60]. Wireless sensing uses the C-band (4.8 GHz) in the indoor environment to monitor body movements of women, especially pregnant women, for early detection of seizure in pre-eclamptic women, so patients can be managed promptly and the mode of delivery can be decided early. The body movement shows unique features extracted from WCSI and can easily be differentiated by using ML classifiers [61]. A bathroom has a comparatively higher probability of severe accidents than other places or rooms due to a slippery floor. A commercial Wi-Fi device-based danger-pose detection system was used while preserving privacy [62]. A non-contact sensing method uses passive Doppler radar to capture human body movements to recognize respiration and other physical activities used for monitoring health. The system uses existing available wireless signals as a source to detect human activity. A two-stage signal processing framework was outlined to support the multi-purpose monitoring functionality. The first stage obtained the primary Doppler information by introducing high speed passive radar signal processing. The second stage functionality was signal processing of micro Doppler extraction data for breathing detection and classification [63].
Parasomnia is a sleep disorder that causes involuntary, random and unwanted movements of a dreaming patient. Unfortunately, these dreams may cause violent activities, which can result in more chances of injury, including that of a bed partner. Continuous monitoring of patients can prevent difficult situations. The system for continuous monitoring of patients exploits fine-grained magnitude and phase information of the WCSI. The variations in the WCSI, as a result of patient body movements, were monitored to identify the behavior [64]. Cerebellar ataxia (CA) is a neurological disease having symptoms of weak coordination movements and balance disorders. A non-contact sensing system was developed for detecting CA based on rapid alternating movements and heel–knee–shin diagnosis tests. This system has the potential to monitor CA in a flexible and patient-friendly environment [65]. A non-contact sensing method uses RF signals to detect paraparesis. It is a promising solution that can reduce the load and improve doctor work efficiency. A system used the 1D-convolution neural network (CNN) model for automatic extraction of valid features and classifications. The system performed efficient and accurate patient screening of suspected paraparesis [66]. Parkinson's disease is a progressive neurologic disorder that primarily affects the movements and limits the motor ability of the patient. Freezing of gait (FOG) is a motor symptom of Parkinson's disease in ageing people, and its timely treatment can reduce the probability of any secondary disorders. The magnitude and phase information of the radio signals is used to detect the motor and non-motor symptoms. The method is very useful with minimum deployment of resources for real-time patient monitoring systems [67].
Cerebellar dysfunction (CD) is one of several neurological disorders that disturbs the movement of the body. A user-friendly system was used to evaluate body movements in CD patients using S-band sensing technique. This system quantified the tremors in hand and gait abnormality using wireless devices such as a NIC, omnidirectional antennas and a router operating at 2.4 GHz to extract the CSI data [68]. Wireless sensor networks (WSNs) use directional antennas extensively for various applications. The four-beam patch antenna was used as a sensor node to evaluate the pill-rolling effect in Parkinson's disease. The four-beam patch is highly directive, small in size and can mitigate the multipath fading present in an indoor environment for effective measurements. The pill-rolling affect indicates the tremors in the hands, predominantly in the fore-finger and the thumb. The developed system was a low-cost framework that evaluates the movement disorder using the S-band sensing platform leveraging wireless devices working at 2.4 GHz. The system efficiently classifies tremor and non-tremor feelings in the fingers [69]. A particular body movement of multiple sclerosis patients is monitored by a C-band sensing system working at 4.8 GHz, and especially the tremors and breathing patterns by a 5 G potential band. This system can identify the particular condition of a patient efficiently [70]. The wireless signal technology successfully detects human motions and related diseases in a non-contact manner [71]. Heart rate and breathing patterns of a person are major indicators of a physical condition. A system was developed for measuring the changes in the heart rate and breathing pattern of a person using commercial Wi-Fi devices. This is an inexpensive system and very useful for monitoring daily life health [72].
Human vital signs of heart rate and breathing along with body posture during sleep is very important to monitor and evaluate general physical health. A system was developed by using off-the-shelf Wi-Fi devices to track the vital signs of both heart rate and breathing rate during sleep without dedicated devices. An existing Wi-Fi network was re-used by the system to exploit the fine-grained CSI to capture every movement caused by heart beats and breathing. This system has the ability to monitor continuously and can be easily deployed everywhere with very cheap solutions [73]. FOG is a periodic absence of forward movement in PD patients, and it is one of the disabilities. A Wi-Freeze is a non-invasive Wi-Fi-based sensing system used for detection and classification of FOG [74]. The monitoring of various physical activities exploits wireless sensing devices, such as sensors used in medical cyber-physical systems (CPS). Patients undergoing epileptic seizures show signs of involuntary body movements. The system exploits S-band operating frequencies used for data extraction and classification of a clinical condition of epileptic seizures [75]. Wi-Fall is a system used for fall detection of independently-living people, especially the elderly. It can detect the fall of the human without any extra hardware setup or any wearable device. The system was implemented using commercial 802.11 n NIC. It can achieve high fall detection accuracy for a single person [76]. A real time (RT)-fall, contactless, inexpensive and accurate fall detection system used commercial Wi-Fi devices. It allowed users to perform routine activities continuously and naturally without attaching any devices on the body [77]. Res-Beat is a commercial Wi-Fi device-based system used for non-contact real-time respiration rate monitoring. The system analyzes bimodal CSI data for breathing signal anomalies to detect peak and estimate respiration rates [78].
The DL-based CNN model classifies ankle movements after surgery using the SDR platform. WCSI image data accurately detected movement of the ankle of patients who suffered fracture ankle surgery [79]. A non-contact sensing testbed was designed using universal software radio peripheral (USRP) devices for the classification of post-surgery activities. The testbed efficiently classified the weight lifting activity of spinal cord patients by exploiting WCSI [80]. Sometimes involuntary scratching may increase the spread of skin diseases such as atopic dermatitis. The frequency of scratching indicates the degree of itching and is helpful in analyzing clinical diagnosis. A system has the potential to monitor the scratching signal of a sleeping human body using a Wi-Fi router and a leaky coaxial cable (LCC) [81]. Hypopnea syndrome is a chronic respiratory disease that is described by repetitive occurrences of breathing disturbances during sleep. A contactless system provides an alternative to conventional medical testing for detecting incognito hypopnea syndrome using S-band wireless sensing. This system has the potential for monitoring accurate hypopnea syndrome in a user-friendly and flexible environment [82]. Respiratory rhythm is the indication of respiratory diseases. The ignored respiratory issues can be dangerous and may cause damage to other body tissues and organs. A non-contact respiratory rhythm detection used an MSP to capture the minute variations caused by breathing. This solution is affordable and its performance is high [83]. LCC has been used extensively in wireless communication to cover blind and semi-blind regions. A system used LCC to identify patient postures in bed in order to prevent or reduce bedsores. The indoor installation and periodic CSI data collection using 802.11 n Intel WLAN NICs helped to monitor postures [84]. A system monitored abnormal breathing patterns caused by sudden infant death syndrome (SIDS) and sleep apnea patients. This system used S-band wireless sensing to extract CSI data for the periodic and non-periodic signals that identify the normal and abnormal respiratory conditions [85]. Traditional, non-contact breathing detection systems required specialized hardware support that is not affordable in normal environments. Non-contact breathing detection systems based on C-band wireless sensing can easily be deployed in any environment. It is based on a multi-input, multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system using 802.11 n protocol [86]. The focus of the 5 G autonomous network used wireless sensing for health care monitoring. The monitoring of respiratory symptoms for COPD (chronic obstructive pulmonary disease) used C-band wireless sensing to detect the respiratory conditions, including coughing and normal and abnormal breathing of a COPD patient by utilizing NIC and the CSI tool for the extraction of CSI with an omni-directional antenna operating at 4.8 GHz frequency. The 5 G sensing technology enhanced the health care system for detection of various diseases effectively [87].
Table 4 summarizes the non-contact sensing technologies, diagnosis of symptoms and monitoring health, classification method and accuracy achieved by the existing research platform. It was revealed from the literature review that the non-contact sensing approach has the potential for the early diagnosis of various symptoms to monitor health, such as breathing, heart rate, fall and sleep disorder. Most systems in the existing literature exploit Wi-Fi technology using CSI to detect and classify the health problems. The SVM algorithm is widely applied because it is applicable to both linear and non-linear data. The classification accuracy achieved by ML and DL algorithms is over 90% [55,56,57,58,59,60,61,62,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80]. The average accuracy achieved by various non-contact sensing technologies to monitor health issues is shown in Figure 2.
3. Proposed Platform
A non-contact wireless platform is proposed on the basis of the existing literature. The five major functional blocks for the development of platforms are data collection devices, WCSI-based data extraction, data preprocessing, features extraction and classification, as shown in Figure 3.
The data can be collected by either specialize or commercial hardware devices. The coughing and breathing data are collected for early diagnosis of COVID-19 symptoms. The data of sitting, standing, walking, sleeping, eating and posture are collected for the monitoring and detection of fall, heart rate, sleep disorder and diet to protect human lives in COVID-19 pandemic.
3.2. Data Extraction
The OFDM signal is used for fine grained WCSI extraction at the receiver. The WCSI frequency response of each activity is monitored continuously, having the information of the number of sub-carriers, the number of samples and the time taken to complete the activity. The time and samples can be expressed as the number of samples received in a unit time. This sampling time can be chosen on the basis of the device sample rate. The total frequency response of WCSI is express in Equation (1):
H ( j ω ) t o t a l = [ H 11 H 12 … H 1 s H 21 H 22 … H 2 s ⋮ ⋮ … ⋮ H k 1 H k 2 … H k s ]
where k represents the maximum number of sub-carriers, and s represents the total number of samples. The WCSI frequency response of single OFDM frame can be expressed as in Equation (2):
H ( j ω ) s u b − c a r r i e r = [ H ( j ω 1 ) , H ( j ω 2 ) , … H ( j ω k ) ]
The WCSI frequency response of each sub-carrier contain amplitude and phase information, it can be expressed as in Equations (2) and (3), respectively:
| H ( j ω k ) | = H ( j ω k ) r e a l 2 + H ( j ω k ) i m g 2
∠ H ( j ω k ) = − t a n − 1 ( H ( j ω k ) i m g H ( j ω k ) r e a l )
| H ( j ω k ) | is the amplitude of the kth subcarrier, and ∠ H ( j ω k ) is the phase of the kth sub-carrier. The amplitude and phase information of WCSI is useful for identifying the human body motion to recognize human activity and health condition.
3.3. Preprocessing
Data preprocessing requires data cleaning, smoothing and grouping to ensure meaningful, accurate and efficient analysis. Data cleaning is a process to remove and replace missing or bad data. It detects abrupt changes and local extrema, which is useful to find significant data trends. The smoothing process remove noise using filtering and other signal processing techniques. The grouping process is used to identify correlations among the data values.
3.4. Features
Feature extraction is a transformation of information, which changes measured data into meaningful information. In addition, it is a dimension reduction process to reduce the computation complexity and time. Presently, statistical characteristics approaches have been used for feature extraction. In the literature, various features are extracted on the basis of data properties to improve the classification performance. According to WCSI data properties, statistical features are divided into two categories, such as the time-domain and frequency-domain [11,88,89,90]. The most important features used for WCSI data are listed in Table 5.
3.5. Classification
Most human activity recognition approaches exploit the ML and DL algorithms to classify the motion type and its corresponding human activity to test the health status. The efficiency of the classifier depends on the type of dataset. ML can be used to develop activity detection models that make health predictions based on WCSI data in the presence of uncertainty. Adaptive algorithms classify normal and abnormal health patterns in the WCSI data. When a learning computer is exposed to more experimental data, the computer improves its identification performance. The entire set of WCSI data is considered as a heterogeneous matrix. The WCSI response data set is a column vector where each row is labeled with the corresponding activity in the WCSI row data. ML model accuracy is used as a diagnostic measure to reflect the validated model results [80]. In DL, CNN learns useful information from images. In the existing literature, it is used for monitoring purposes in many research studies. Currently, DL is efficiently applied in the biomedical area. AlexNet and ZFNet are the most popular CNN architectures and can be used in a parallel manner to classify WCSI numeric data that is converted to images [79].
4. Experimental Setup
On the basis of the literature review, an experimental setup is proposed for the development of a COVID-19 platform. In the following, two hardware setups are proposed to conduct experiments in a bedroom along with a bathroom to capture RT environments.
4.1. Commercial Hardware Platform
This hardware platform consists of a Wi-Fi router, NIC, desktop PC or laptop and omni-directional antennas, as shown in Figure 4a. It utilizes a CSI tool inbuilt on the Intel Wi-Fi wireless link (IWL) 802.11 n MIMO radio, which uses an open source Linux wireless driver and custom modified firmware. It includes all the software to read and parse the channel measurements and scripts needed to run experiments. The IWL provides 802.11 n standard WCSI in a data format that reports the channel matrices of 30 sub-carrier groups, which is about one group for every 2 sub-carriers at 20 MHz or one group for every 4 sub-carriers at 40 MHz frequency. Each channel matrix entry is a complex number, with signed 8-bit resolution, each for the real and imaginary parts. It specifies the gain and phase of the signal path between a single transmit–receive antenna pair. The hardware setup is inexpensive, easily accessible and commercially available [91,92].
4.2. Specialized Hardware Platform
This hardware platform consists of two USRP devices, one for transmission and the other for reception, along with omni-directional antennas and a desktop PC or laptop, as shown in Figure 4b. Currently, different SDR platforms are used for experimental research; among them, the USRP manufactured by Ettus research is mostly used, which has become the standard choice for wireless research [93,94]. The architecture incorporates the Xilinx Spartan-6 FPGA along with the agile analog radio frequency integrated circuits (RFIC)'s direct adaptation transceiver. The RFIC determines the number of independent transceivers. It integrates independent coherent transceivers that allow implementation of n × n MIMO systems. This device can cover a wide range of frequency coverage with an adjustable bandwidth and can run in frequency division duplex (FDD) or time division duplex (TDD) mode; it allows the FDD to operate in full-duplex mode, while the TDD operates in half-duplex mode [95]. This hardware setup is flexible and portable.
Various experiments can be carried out on commercial and specialized hardware platforms to develop fully functional non-contact sensing COVID-19 platform. Experiments are to be divided into two main categories:
Symptoms data collection
Initially, coughing and shortness of breath data are collected from patients for developing a ML/DL model to classify suspected patients of COVID-19. From the literature review [40,45,48,57,63,70], both coughing and breathing can be monitored in a non-contact manner.
Activities data collection
It is very essential to recognize human activities for stability during the isolation and quarantine period. Standing, walking, sleeping, eating, bathing and postures data are collected for developing a ML/DL model to recognize the fall, sleep disorder and diet of the patient. From the literature review, heart problems, fall, sleep disorder and eating habits can be recognized by non-contact wireless sensing platforms [14,15,17,20,22,23,25,26,27,28,30,31,33,34,36,38,40,45,47,48,49,57,61,62,63].
5. Outcomes
The following are the outcomes from the development of a non-contact sensing platform for containing of COVID-19.
Wireless signals can pass through the wall and do not require LOS. This feature of non-contact sensing eliminates the need for face-to-face contact and provides improved management to contain COVID-19.
In case COVID-19 symptoms develop, the data sent by means of cloud computing platforms can enable healthcare authorities to respond quickly.
It will reduce the physical contact time with a COVID-19 patient as much as possible.
It will not only monitor COVID-19 symptoms but also continuous health monitoring during quarantine and isolation periods in a non-contact manner.
Transferring care to home, or treating high-risk elders and children in their own homes.
This will improve privacy of individuals during quarantine or isolation periods.
It will also help in early recognition of patients who need aggressive management or hospitalization to prevent them from serious or irreversible sequelae of the disease.
Reduce life risk of doctors, paramedical staff and caretakers during quarantine and isolation periods.
Innovative tools to construct useful contactless sensing platforms for health care applications.
These platforms can be deployed by re-using the existing infrastructure of wireless communication networks.
Improved access to care, increased quality of care and reduced care costs.
It can be deployed in any emergency condition at any place to counter health challenges.
6. Challenges
Although the literature review has demonstrated the potential capabilities for developing a non-contact sensing platform for the monitoring of COVID-19 to contain the virus and protect humanity, there are still existing challenges and research problems that need further investigation and exploration.
Environmental effect
The real time environment is challenging for developing a classification model using WCSI. The environment varies from place to place due to furniture movement, closing and opening of windows and doors, electronic devices, etc., which leads to changes of the behavior of the wireless channel. It is necessary to develop a model which can adopt to new environment.
Experimental subject
One of the biggest challenges in data collection is the subject used in the medical related experiments. It is very difficult to use real patients in all the experiments. Diseases and health status vary greatly from patient to patient and during different time periods. With COVID-19, early symptoms also vary from patient to patient. On the other hand, experimenting with real patients is not comfortable for them and also requires time to perform extensive experiments. Researchers mainly used healthy subjects for performing experiments, which may not address the actual problem.
Orientation and location
The user's orientation and location also have critical effects on the performance of WCSI-based sensing systems. The differences in users' orientation and location may cause different variations in WCSI measurements. Existing research mainly used the same orientation and location during experiments. However, a few research studies considered different orientations and locations to overcome such limitations.
Multi-subject sensing
Most of the research on WCSI-based sensing platforms considered a single subject for investigation. It is challenging to differentiate the movements of multiple subjects using WCSI measurements. Considering the COVID-19 isolation or quarantine period guidelines, a single subject is enough for developing the platform. However, early diagnosis of COVID-19 symptoms requires multi-subject sensing, because before diagnosing COVD-19 as positive, people are not quarantined from their families and are living in a multi-subject environment. This stage is difficult to monitor and is the main source of the viral spread.
With the rapid expansion of Wi-Fi sensing technology, it also raises privacy and security issues. Existing research has demonstrated that Wi-Fi signals can interfere with other users. Enemies may spy the activities and position using existing human activity sensing systems. It is necessary to pay more attention to the improved privacy and security concerns.
7. Future Recommendations
The following are the future recommendations to improve the non-contact sensing platform for the monitoring of human activities and health conditions to contain COVID-19:
It is recommended to perform extensive experiments with different environments and experimental setups to develop an RT model.
It is recommended to collect experimental data by using multi-subjects with extensive experimentation to develop a model.
An efficient and possible solution must develop a rigorous theoretical model independent of the user's location and orientation; the correct mapping of the relationship between WCSI measurements and the human body motions identify the health conditions. It is recommended to conduct experiments with different orientations and locations for the collection of data for developing models.
It is recommended to extract more prominent features to differentiate human activities and health conditions. Frequency domain features are useful for classifying multi-subjects.
It is recommended to use SDR-based WCSI sensing to counter the privacy and security using a self-generated signal approach that can switch to different frequency bands.
Various measurements and research studies are initiated to contain COVID-19 throughout the world. Limiting human-to-human contact is the best solution to reduce the spread of COVID-19. This research presents a comprehensive review on existing non-contact sensing of human activities and health monitoring that could be used for the development of a COVID-19 pandemic platform. The Wi-Fi and SDR technology has the potential to contain COVID-19 in a non-contact manner. This study proposes a non-contact WCSI-based sensing platform for monitoring COVID-19 to contain the deadly pandemic situation.
The proposed platform has the potential to diagnose the early symptoms like coughing and shortness of breath. The development of the platform is very useful in the quarantine and isolation period because it will monitor fall, sleep disorder, shortness of breathing, coughing level, heartbeat and diet of suspected or confirmed COVID-19 cases. Although the proposed platform is a promising solution, there still exist limitations to achieve optimal performance. This study highlights the challenges, and it is expected that proposed solutions will contribute to contain COVID-19.
Conceptualization, M.B.K.; methodology, M.B.K.; validation, M.B.K.; formal analysis, M.B.K.; investigation, Z.Z.; resources, X.Y.; data curation, W.Z.; writing—original draft preparation, M.B.K. and M.A.M.A.H.; writing—review and editing, L.L.; supervision, X.Y. and Q.H.A.; project administration, X.Y.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.
National Natural Science Foundation of Shaanxi Province (No. 2019JQ-579), National Natural Science Foundation of China 61301175.
World Health Organization. Considerations for Quarantine of Individuals in the Context of Containment for Coronavirus Disease (COVID-19): Interim Guidance, 29 February 2020; World Health Organization: Geneva, Switzerland, 2020. [Google Scholar]
Nicola, M.; O'Neill, N.; Sohrabi, C.; Khan, M.; Agha, M.; Agha, R. Evidence based management guideline for the COVID-19 pandemic—Review article. Int. J. Surg. 2020, 77, 206–216. [Google Scholar] [CrossRef]
Hussain, Z.; Sheng, Q.Z.; Zhang, W.E. Different Approaches for Human Activity Recognition: A Survey. arXiv 2019, arXiv:1906.05074. [Google Scholar]
Liu, J.; Teng, G.; Hong, F. Human Activity Sensing with Wireless Signals: A Survey. Sensors 2020, 20, 1210. [Google Scholar] [CrossRef][Green Version]
Ma, J.; Wang, H.; Zhang, D.; Wang, Y.; Wang, Y. A Survey on Wi-Fi Based Contactless Activity Recognition. In Proceedings of the 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), Toulouse, France, 18–21 July 2016; pp. 1086–1091. [Google Scholar]
Khan, M.B.; Yang, X.; Ren, A.; Al-Hababi, M.A.M.; Zhao, N.; Guan, L.; Fan, D.; Shah, S.A. Design of Software Defined Radios Based Platform for Activity Recognition. IEEE Access 2019, 7, 31083–31088. [Google Scholar] [CrossRef]
Chávez-Santiago, R.; Mateska, A.; Chomu, K.; Gavrilovska, L.; Balasingham, I. Applications of software-defined radio (SDR) technology in hospital environments. In Proceedings of the 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3–7 July 2013; pp. 1266–1269. [Google Scholar]
Ma, Y.; Zhou, G.; Wang, S. WiFi Sensing with Channel State Information. ACM Comput. Surv. 2019, 52, 46. [Google Scholar] [CrossRef][Green Version]
Liu, J.; Liu, H.; Chen, Y.; Wang, Y.; Wang, C. Wireless Sensing for Human Activity: A Survey. IEEE Commun. Surv. Tutor. 2020, 22, 1629–1645. [Google Scholar] [CrossRef]
Xin, T.; Guo, B.; Wang, Z.; Li, M.; Yu, Z.; Zhou, X. FreeSense: Indoor Human Identification with Wi-Fi Signals. In Proceedings of the 2016 IEEE Global Communications Conference (GLOBECOM), Washington, DC, USA, 4–8 December 2016; pp. 1–7. [Google Scholar]
Wang, Z.; Jiang, K.; Hou, Y.; Huang, Z.; Dou, W.; Zhang, C.; Guo, Y. A Survey on CSI-Based Human Behavior Recognition in Through-the-Wall Scenario. IEEE Access 2019, 7, 78772–78793. [Google Scholar] [CrossRef]
He, Y.; Chen, Y.; Hu, Y.; Zeng, B. WiFi Vision: Sensing, Recognition, and Detection with Commodity MIMO-OFDM WiFi. IEEE Internet Things J. 2020, 7, 8296–8317. [Google Scholar] [CrossRef]
Cascella, M.; Rajnik, M.; Cuomo, A.; Dulebohn, S.C.; di Napoli, R. Features, Evaluation and Treatment Coronavirus (COVID-19) Updated 6 April 2020. In StatPearls [Internet]; StatPearls Publishing: Treasure Island, FL, USA, 2020. Available online: https://www.ncbi.nlm.nih.gov/books/NBK554776/ (accessed on 28 September 2020).
Sohrabi, C.; Alsafi, Z.; O'Neill, N.; Khan, M.; Kerwan, A.; Al-Jabir, A.; Iosifidis, C.; Agha, R. World Health Organization declares global emergency: A review of the 2019 novel coronavirus (COVID-19). Int. J. Surg. 2020, 76, 71–76. [Google Scholar] [CrossRef]
Chamola, V.; Hassija, V.; Gupta, V.; Guizani, M. A Comprehensive Review of the COVID-19 Pandemic and the Role of IoT, Drones, AI, Blockchain, and 5G in Managing its Impact. IEEE Access 2020, 8, 90225–90265. [Google Scholar] [CrossRef]
WHO. Report WHO-China Joint Mission Coronavirus Disease 2019 (COVID-19). Available online: https://www.who.int/docs/default-source/coronaviruse/who-chinajoint-mi%ssion-on-covid-19-final-report.pdf (accessed on 28 September 2020).
People Who Are at Higher Risk for Severe Illness. Centers for Disease Control Prevention (CDC). Available online: https://www.cdc.gov/coronavirus/2019-ncov/needextraprecautions/people%-at-higher-risk.html (accessed on 28 September 2020).
World Health Organization. Clinical management of severe acute respiratory infection (SARI) when COVID-19 disease is suspected. Interim guidance. Pediatr. Med. Rodz. 2020, 16, 9–26. [Google Scholar] [CrossRef]
Zhang, Z. Respiratory Laryngeal Coordination in Airflow Conservation and Reduction of Respiratory Effort of Phonation. J. Voice 2016, 30, 760.e7–760.e13. [Google Scholar] [CrossRef][Green Version]
Gramming, P.; Sundberg, J.; Ternstrom, S.; Leanderson, R.; Perkins, W.H. Relationship between changes in voice pitch and loudness. J. Voice 1988, 2, 118–126. [Google Scholar] [CrossRef]
Gracco, V.; Löfqvist, A. Speech motor coordination and control: Evidence from lip, jaw, and laryngeal movements. J. Neurosci. 1994, 14, 6585–6597. [Google Scholar] [CrossRef][Green Version]
Mao, L.; Wang, M.; Chen, S.; He, Q.; Chang, J.; Hong, C.; Zhou, Y.; Wang, D.; Miao, X.; Hu, Y.; et al. Neurological Manifestations of Hospitalized Patients with COVID-19 in Wuhan, China: A Retrospective Case Series Study. SSRN Electron. J. 2020. [Google Scholar] [CrossRef]
Helms, J.; Kremer, S.; Merdji, H.; Clere-Jehl, R.; Schenck, M.; Kummerlen, C.; Collange, O.; Boulay, C.; Fafi-Kremer, S.; Ohana, M.; et al. Neurologic Features in Severe SARS-CoV-2 Infection. N. Engl. J. Med. 2020, 382, 2268–2270. [Google Scholar] [CrossRef]
Yan, C.H.; Faraji, F.; Bs, D.P.P.; Boone, C.E.; DeConde, A.S. Association of chemosensory dysfunction and COVID-19 in patients presenting with influenza-like symptoms. Int. Forum Allergy Rhinol. 2020. [Google Scholar] [CrossRef][Green Version]
Gautier, J.-F.; Ravussin, Y. A New Symptom of COVID-19: Loss of Taste and Smell. Obesity 2020, 28, 848. [Google Scholar] [CrossRef][Green Version]
Quatieri, T.; Talkar, T.; Palmer, J. A Framework for Biomarkers of COVID-19 Based on Coordination of Speech-Production Subsystems. IEEE Open J. Eng. Med. Boil. 2020, 1, 203–206. [Google Scholar] [CrossRef]
World Health Organization. Modes of Transmission of Virus Causing COVID-19: Implications for IPC Precaution Recommendations. Available online: https://www.who.int/newsroom/commentaries/detail/modes-of-transmission%-of-virus:causingcovid-19-implications-for-ipc-precaution-recommendations (accessed on 28 September 2020).
Qian, K.; Wu, C.; Yang, Z.; Liu, Y.; He, F.; Xing, T. Enabling Contactless Detection of Moving Humans with Dynamic Speeds Using CSI. ACM Trans. Embed. Comput. Syst. 2018, 17, 52. [Google Scholar] [CrossRef]
Sigg, S.; Scholz, M.; Shi, S.; Ji, Y.; Beigl, M. RF-Sensing of Activities from Non-Cooperative Subjects in Device-Free Recognition Systems Using Ambient and Local Signals. IEEE Trans. Mob. Comput. 2013, 13, 907–920. [Google Scholar] [CrossRef]
Wang, W.; Liu, A.X.; Shahzad, M.; Ling, K.; Lu, S. Device-Free Human Activity Recognition Using Commercial WiFi Devices. IEEE J. Sel. Areas Commun. 2017, 35, 1118–1131. [Google Scholar] [CrossRef]
Di Domenico, S.; De Sanctis, M.; Cianca, E.; Ruggieri, M. WiFi-based through-the-wall presence detection of stationary and moving humans analyzing the doppler spectrum. IEEE Aerosp. Electron. Syst. Mag. 2018, 33, 14–19. [Google Scholar] [CrossRef]
Damodaran, N.; Haruni, E.; Kokhkharova, M.; Schäfer, J. Device free human activity and fall recognition using WiFi channel state information (CSI). CCF Trans. Pervasive Comput. Interact. 2020, 2, 1–17. [Google Scholar] [CrossRef][Green Version]
Taylor, W.; Shah, S.A.; Dashtipour, K.; Zahid, A.; Abbasi, Q.; Imran, M. An Intelligent Non-Invasive Real-Time Human Activity Recognition System for Next-Generation Healthcare. Sensors 2020, 20, 2653. [Google Scholar] [CrossRef]
Sharma, S.; Mohammadmoradi, H.; Heydariaan, M.; Gnawali, O. Device-Free Activity Recognition Using Ultra-Wideband Radios. In Proceedings of the 2019 International Conference on Computing, Networking and Communications (ICNC), Honolulu, HI, USA, 18–21 February 2019; pp. 1029–1033. [Google Scholar]
Al-Qaness, M.A.; Li, F. WiGeR: WiFi-Based Gesture Recognition System. ISPRS Int. J. GeoInf. 2016, 5, 92. [Google Scholar] [CrossRef][Green Version]
Li, X.; Zhang, D.; Xiong, J.; Zhang, Y.; Li, S.; Wang, Y.; Mei, H. Training-Free Human Vitality Monitoring Using Commodity Wi-Fi Devices. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2018, 2, 121. [Google Scholar] [CrossRef]
Wang, C.; Lin, Z.; Xie, Y.; Guo, X.; Ren, Y.; Chen, Y. WiEat: Fine-grained Device-free Eating Monitoring Leveraging Wi-Fi Signals. arXiv 2020, arXiv:2003.09096. [Google Scholar]
Pu, Q.; Gupta, S.; Gollakota, S.; Patel, S. Whole-home gesture recognition using wireless signals. In Proceedings of the 19th Annual International Conference on Mobile Computing & Networking, Miami, FL, USA, 30 September–4 October 2013; pp. 27–38. [Google Scholar]
Wang, G.; Zou, Y.; Zhou, Z.; Wu, K.; Ni, L.M. We Can Hear You with Wi-Fi! IEEE Trans. Mob. Comput. 2016, 15, 2907–2920. [Google Scholar] [CrossRef]
Zhu, S.; Xu, J.; Guo, H.; Liu, Q.; Wu, S.; Wang, H. Indoor Human Activity Recognition Based on Ambient Radar with Signal Processing and Machine Learning. In Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA, 20–24 May 2018; pp. 1–6. [Google Scholar]
Liu, J.; Han, J.; Lin, F.; Ren, K. Adversary Helps: Gradient-based Device-Free Domain-Independent Gesture Recognition. arXiv 2020, arXiv:2004.03961. [Google Scholar]
Çağlıyan, B.; Gürbüz, S.Z. Micro-Doppler-Based Human Activity Classification Using the Mote-Scale BumbleBee Radar. IEEE Geosci. Remote. Sens. Lett. 2015, 12, 2135–2139. [Google Scholar] [CrossRef]
Zhou, Q.; Xing, J.; Yang, Q. Device-free occupant activity recognition in smart offices using intrinsic Wi-Fi components. Build. Environ. 2020, 172, 106737. [Google Scholar] [CrossRef]
Guo, L.; Guo, S.; Wang, L.; Lin, C.; Liu, J.; Lu, B.; Fang, J.; Liu, Z.; Shan, Z.; Yang, J. Wiar: A Public Dataset for Wifi-Based Activity Recognition. IEEE Access 2019, 7, 154935–154945. [Google Scholar] [CrossRef]
Li, H.; He, X.; Chen, X.; Fang, Y.; Fang, Q. Wi-Motion: A Robust Human Activity Recognition Using WiFi Signals. IEEE Access 2019, 7, 153287–153299. [Google Scholar] [CrossRef]
Shah, S.A.; Ahmad, J.; Tahir, A.; Ahmed, F.; Russell, G.; Buchanan, W.J.; Abbasi, Q.; Shah, S. Privacy-Preserving Non-Wearable Occupancy Monitoring System Exploiting Wi-Fi Imaging for Next-Generation Body Centric Communication. Micromachines 2020, 11, 379. [Google Scholar] [CrossRef] [PubMed][Green Version]
Luo, F.; Poslad, S.; Bodanese, E. Kitchen Activity Detection for Healthcare using a Low-Power Radar-Enabled Sensor Network. In Proceedings of the ICC 2019—2019 IEEE International Conference on Communications (ICC), Shanghai, China, 20–24 May 2019; pp. 1–7. [Google Scholar]
Shi, Z.; Zhang, J.A.; Xu, R.; Fang, G. Human Activity Recognition Using Deep Learning Networks with Enhanced Channel State Information. In Proceedings of the 2018 IEEE Globecom Workshops (GC Wkshps), Abu Dhabi, UAE, 9–13 December 2018; pp. 1–6. [Google Scholar] [CrossRef]
Kim, S.-C.; Kim, T.G.; Kim, S.H. Human Activity Recognition and Prediction Based on Wi-Fi Channel State Information and Machine Learning. In Proceedings of the 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Okinawa, Japan, 11–13 February 2019; pp. 418–422. [Google Scholar]
Cheng, L.; Wang, J. Walls Have No Ears: A Non-Intrusive WiFi-Based User Identification System for Mobile Devices. IEEE/ACM Trans. Netw. 2019, 27, 245–257. [Google Scholar] [CrossRef]
Gu, Y.; Wang, Y.; Liu, Z.; Liu, J.; Li, J. SleepGuardian: An RF-Based Healthcare System Guarding Your Sleep from Afar. IEEE Netw. 2020, 34, 164–171. [Google Scholar] [CrossRef][Green Version]
Liu, M.; Zhang, L.; Yang, P.; Lu, L.; Gong, L. Wi-Run: Device-free step estimation system with commodity Wi-Fi. J. Netw. Comput. Appl. 2019, 143, 77–88. [Google Scholar] [CrossRef]
Wang, F.; Gong, W.; Liu, J. On Spatial Diversity in WiFi-Based Human Activity Recognition: A Deep Learning-Based Approach. IEEE Internet Things J. 2018, 6, 2035–2047. [Google Scholar] [CrossRef]
Li, X.; He, Y.; Jing, X. A Survey of Deep Learning-Based Human Activity Recognition in Radar. Remote. Sens. 2019, 11, 1068. [Google Scholar] [CrossRef][Green Version]
Al-Momani, O.; Gharaibeh, K.M. Effect of Wireless Channels on Detection and Classification of Asthma Attacks in Wireless Remote Health Monitoring Systems. Int. J. Telemed. Appl. 2014, 2014, 816369. [Google Scholar] [CrossRef][Green Version]
Haider, D.; Yang, X.; Abbasi, Q.H. Post-surgical fall detection by exploiting the 5 G C-Band technology for eHealth paradigm. Appl. Soft Comput. 2019, 81, 105537. [Google Scholar] [CrossRef]
Zhu, Q.; Guan, L.; Khan, M.B.; Yang, X. Monitoring of Huntington's Disease Based on Wireless Sensing Technology. Appl. Sci. 2020, 10, 870. [Google Scholar] [CrossRef][Green Version]
Yang, X.; Fan, D.; Ren, A.; Zhao, N.; Zhang, Z.; Haider, D.; Khan, M.B.; Tian, J. Non-Contact Early Warning of Shaking Palsy. IEEE J. Transl. Eng. Heal. Med. 2019, 7, 1800408. [Google Scholar] [CrossRef] [PubMed]
Yang, X.; Shah, S.A.; Ren, A.; Zhao, N.; Fan, D.; Hu, F.; Rehman, M.U.; Von Deneen, K.M.; Tian, J. Wandering Pattern Sensing at S-Band. IEEE J. Biomed. Health Inform. 2018, 22, 1863–1870. [Google Scholar] [CrossRef] [PubMed]
Khan, U.M.; Kabir, Z.; Hassan, S.A. Wireless health monitoring using passive WiFi sensing. In Proceedings of the 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC), Valencia, Spain, 26–30 June 2017; pp. 1771–1776. [Google Scholar]
Haider, D.; Ren, A.; Fan, D.; Zhao, N.; Yang, X.; Shah, S.A.; Hu, F.; Abbasi, Q.H. An efficient monitoring of eclamptic seizures in wireless sensors networks. Comput. Electr. Eng. 2019, 75, 16–30. [Google Scholar] [CrossRef]
Zhang, Z.; Ishida, S.; Tagashira, S.; Fukuda, A. Danger-Pose Detection System Using Commodity Wi-Fi for Bathroom Monitoring. Sensors 2019, 19, 884. [Google Scholar] [CrossRef][Green Version]
Li, W.; Tan, B.; Piechocki, R. Passive Radar for Opportunistic Monitoring in E-Health Applications. IEEE J. Transl. Eng. Health Med. 2018, 6, 2800210. [Google Scholar] [CrossRef]
Yang, X.; Shah, S.A.; Ren, A.; Zhao, N.; Zhao, J.; Hu, F.; Zhang, Z.-Y.; Zhao, W.; Ur-Rehman, M.; Alomainy, A. Monitoring of Patients Suffering From REM Sleep Behavior Disorder. IEEE J. Electromagn. RF Microw. Med. Boil. 2018, 2, 138–143. [Google Scholar] [CrossRef]
Jin, J.; Sun, W.; Al-Turjman, F.; Khan, M.B.; Yang, X. Activity Pattern Mining for Healthcare. IEEE Access 2020, 8, 56730–56738. [Google Scholar] [CrossRef]
Yang, X.; Hu, F.; Al-Turjman, F.; Khan, M.B.; Yang, X. A Non-Contact Paraparesis Detection Technique Based on 1D-CNN. IEEE Access 2019, 7, 182280–182288. [Google Scholar] [CrossRef]
Yang, X.; Shah, S.A.; Ren, A.; Zhao, N.; Zhang, Z.-Y.; Fan, D.; Zhao, J.; Wang, W.; Ur-Rehman, M. Freezing of Gait Detection Considering Leaky Wave Cable. IEEE Trans. Antennas Propag. 2019, 67, 554–561. [Google Scholar] [CrossRef]
Yang, X.; Shah, S.A.; Ren, A.; Fan, D.; Zhao, N.; Zheng, S.; Zhao, W.; Wang, W.; Soh, P.J.; Abbasi, Q. $ S $-band sensing-based motion assessment framework for cerebellar dysfunction patients. IEEE Sens. J. 2019, 19, 8460–8467. [Google Scholar] [CrossRef][Green Version]
Shah, S.A.; Yang, X.; Abbasi, Q.H. Cognitive health care system and its application in pill-rolling assessment. Int. J. Numer. Model. Electron. Netw. Devices Fields 2019, 32, e2632. [Google Scholar] [CrossRef]
Haider, D.; Ren, A.; Fan, D.; Zhao, N.; Yang, X.; Tanoli, S.A.K.; Zhang, Z.-Y.; Hu, F.; Shah, S.A.; Abbasi, Q. Utilizing a 5G spectrum for health care to detect the tremors and breathing activity for multiple sclerosis. Trans. Emerg. Telecommun. Technol. 2018, 29, e3454. [Google Scholar] [CrossRef]
Barua, A.; Yang, X.; Ren, A.; Fan, D.; Guan, L.; Zhao, N.; Haider, D. Gait signals classification and comparison. Int. J. Numer. Model. Electron. Netw. Devices Fields 2019, 32, e2577. [Google Scholar] [CrossRef]
Lee, S.; Park, Y.-D.; Suh, Y.-J.; Jeon, S. Design and implementation of monitoring system for breathing and heart rate pattern using WiFi signals. In Proceedings of the 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 12–15 January 2018; pp. 1–7. [Google Scholar]
Liu, J.; Chen, Y.; Wang, Y.; Chen, X.; Cheng, J.; Yang, J. Monitoring Vital Signs and Postures During Sleep Using WiFi Signals. IEEE Internet Things J. 2018, 5, 2071–2084. [Google Scholar] [CrossRef]
Tahir, A.; Ahmad, J.; Shah, S.A.; Morison, G.; A Skelton, D.; Larijani, H.; Abbasi, Q.; Imran, M.; Gibson, R.M. WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson's Leveraging 5G Spectrum with Deep Learning. Electron. 2019, 8, 1433. [Google Scholar] [CrossRef][Green Version]
Shah, S.A.; Fan, D.; Ren, A.; Zhao, N.; Yang, X.; Tanoli, S.A.K. Seizure episodes detection via smart medical sensing system. J. Ambient. Intell. Humaniz. Comput. 2018, 1–13. [Google Scholar] [CrossRef][Green Version]
Wang, Y.; Wu, K.; Ni, L.M. Wifall: Device-free fall detection by wireless networks. IEEE Trans. Mob. Comput. 2016, 16, 581–594. [Google Scholar] [CrossRef]
Wang, H.; Zhang, D.; Wang, Y.; Ma, J.; Wang, Y.; Li, S. RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices. IEEE Trans. Mob. Comput. 2017, 16, 511–526. [Google Scholar] [CrossRef]
Wang, X.; Yang, C.; Mao, S. Resilient Respiration Rate Monitoring With Realtime Bimodal CSI Data. IEEE Sens. J. 2020, 20, 10187–10198. [Google Scholar] [CrossRef]
Barua, A.; Zhang, Z.-Y.; Al-Turjman, F.; Yang, X. Cognitive Intelligence for Monitoring Fractured Post-Surgery Ankle Activity Using Channel Information. IEEE Access 2020, 8, 112113–112129. [Google Scholar] [CrossRef]
Al-Hababi, M.A.M.; Khan, M.B.; Al-Turjman, F.; Zhao, N.; Yang, X. Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence. Appl. Sci. 2020, 10, 4886. [Google Scholar] [CrossRef]
Dong, B.; Ren, A.; Shah, S.A.; Hu, F.; Zhao, N.; Yang, X.; Haider, D.; Zhang, Z.; Zhao, W.; Abbasi, Q.H. Monitoring of atopic dermatitis using leaky coaxial cable. Health Technol. Lett. 2017, 4, 244–248. [Google Scholar] [CrossRef] [PubMed]
Yang, X.; Fan, D.; Ren, A.; Zhao, N.; Shah, S.A.; Alomainy, A.; Ur-Rehman, M.; Abbasi, Q.H. Diagnosis of the Hypopnea syndrome in the early stage. Neural Comput. Appl. 2019, 32, 855–866. [Google Scholar] [CrossRef][Green Version]
Fan, D.; Ren, A.; Zhao, N.; Yang, X.; Zhang, Z.-Y.; Shah, S.A.; Hu, F.; Abbasi, Q. Breathing Rhythm Analysis in Body Centric Networks. IEEE Access 2018, 6, 32507–32513. [Google Scholar] [CrossRef]
Shah, S.A.; Zhao, N.; Ren, A.; Zhang, Z.; Yang, X.; Yang, J.; Zhao, W. Posture Recognition to Prevent Bedsores for Multiple Patients Using Leaking Coaxial Cable. IEEE Access 2016, 4, 8065–8072. [Google Scholar] [CrossRef]
Liu, L.; Shah, S.A.; Zhao, G.; Yang, X. Respiration Symptoms Monitoring in Body Area Networks. Appl. Sci. 2018, 8, 568. [Google Scholar] [CrossRef][Green Version]
Guan, H.; Yang, X.; Sun, W.; Ren, A.; Fan, D.; Zhao, N.; Guan, L.; Haider, D.; Abbasi, Q.H. Posture-Specific Breathing Detection. Sensors 2018, 18, 4443. [Google Scholar] [CrossRef][Green Version]
Zhang, Q.; Haider, D.; Wang, W.; Shah, S.A.; Yang, X.; Abbasi, Q. Chronic Obstructive Pulmonary Disease Warning in the Approximate Ward Environment. Appl. Sci. 2018, 8, 1915. [Google Scholar] [CrossRef][Green Version]
Zheng, X.; Wang, J.; Shangguan, L.; Zhou, Z.; Liu, Y. Smokey: Ubiquitous smoking detection with commercial WiFi infrastructures. In Proceedings of the IEEE INFOCOM 2016—The 35th Annual IEEE International Conference on Computer Communications, San Francisco, CA, USA, 10–14 April 2016; pp. 1–9. [Google Scholar]
Chowdhury, T.Z.; Leung, C.; Miao, C.Y. WiHACS: Leveraging WiFi for human activity classification using OFDM subcarriers' correlation. In Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, QC, Canada, 14–16 November 2017; pp. 338–342. [Google Scholar]
Lu, Y.; Lv, S.H.; Wang, X.D.; Zhou, X.M. A Survey on WiFi based human behavior analysis technology. Chin. J. Comput. 2018, 41, 1–22. [Google Scholar]
Wang, C.; Chen, S.; Yang, Y.; Hu, F.; Liu, F.; Wu, J. Literature review on wireless sensing-Wi-Fi signal-based recognition of human activities. Tsinghua Sci. Technol. 2018, 23, 203–222. [Google Scholar] [CrossRef]
Wyglinski, A.M.; Orofino, D.P.; Ettus, M.N.; Rondeau, T.W. Revolutionizing software defined radio: Case studies in hardware, software, and education. IEEE Commun. Mag. 2016, 54, 68–75. [Google Scholar] [CrossRef]
El-Hajjar, M.; Nguyen, Q.A.; Maunder, R.G.; Ng, S.X. Demonstrating the practical challenges of wireless communications using USRP. IEEE Commun. Mag. 2014, 52, 194–201. [Google Scholar] [CrossRef][Green Version]
Welch, T.B.; Shearman, S. Teaching software defined radio using the USRP and LabVIEW. In Proceedings of the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, 25–30 March 2012; pp. 2789–2792. [Google Scholar]
Khan, M.B.; Dong, C.; Al-Hababi, M.A.M.; Yang, X. Design of a portable and multifunctional dependable wireless communication platform for smart health care. Ann. Telecommun. 2020, 1–10. [Google Scholar] [CrossRef]
Figure 1. Average accuracy of non-contact sensing technologies for monitoring human activities.
Figure 2. Average accuracy of non-contact sensing technologies for monitoring health.
Figure 3. Non-contact sensing platform for diagnosis and monitoring.
Figure 4. Proposed experimental setup: (a) commercial hardware platform; (b) specialize hardware platform.
Table 1. List of abbreviations used.
AP Access point MIMO Multiple input multiple output
BMS Building management systems ML Machine learning
CA Cerebellar ataxia MMP Mouth motion profile
CAF Cross-ambiguity function MSP Microwave sensing platform
CARM Channel state information-based human activity recognition and monitoring NIC Network interface card
CD Cerebellar dysfunction OAR Occupant activity recognition
CDC Centers for Disease Control and Prevention OFDM Orthogonal frequency Division multiplexing
CFES CSI feature enhancement scheme PADS Passive detection of moving humans with dynamic speed
CFR Channel frequency response REM Rapid eye movement
CNN Convolutional neural network RF Radio frequency
COPD Chronic obstructive pulmonary disease RFA Random forest algorithm
COVID Corona virus disease RFIC Radio frequency integrated circuits
RNN Recurrent neural network
CSI Channel state information RT Real time
DG Domain gap SARS-CoV-2 Severe acute respiratory syndrome coronavirus-2
DI Domain independent SDAR Spatial diversity aware non-contact activity recognition
DL Deep learning SDR Software defined radios
DTW Dynamic time warping SIDS Sudden infant death syndrome
EWMA Exponentially weighted moving average SP Shaking palsy
FDD Frequency division duplex SSF Stable signal fusion
FDTW Fast dynamic time warping SVM Support vector machine
FOG Freezing of gait TDD Time division duplex
FPGA Field programmable gate array TTW Through the walls
FRTCS Fast and robust target Component separation USRP Universal software radio peripheral
HAR Human activity recognition UWB Ultra-wide band
HD Huntington's disease WCSI Wireless channel state information
HMM Hidden Markov model Wi-AR Wireless activity recognition
IoT Internet of things Wi-Fi Wireless fidelity
IWL Intel Wi-Fi wireless link Wi-GeR Wi-Fi-based gesture recognition
KNN K-nearest neighbors Wi-Hear Wireless Hear
LCC Leaky coaxial cable Wi-Motion Wireless Motion
LOS Line of sight Wi-See Wireless See
LSTM Long short term memory Wi-Vit Wireless Vitality
MCP Medical cyber–physical WSN Wireless sensor networks
Table 2. Summary of COVID-19 symptoms.
Fever Most common
Cough Most common
Sore throat Less common
Shortness of breath Less common
Fatigue Most common
Aches and pains Rare
Headaches Most common
Runny or stuffy nose Rare
Diarrhea Rare
Table 3. Summary of existing contributions in human activity classification and monitoring.
Technology/Reference
Detection and Monitoring
1 Wi-Fi sensing [28] Moving human SVM 99%
2 SDR [29] Standing, walking, crawling, lying and empty KNN 85%
3 Wi-Fi sensing [30] Walking, running sitting and falling, opening, empty, refrigerator, boxing, pushing one hand, brushing teeth CSI-speed and
CSI-activity model 96%
4 Wi-Fi sensing [31] Human presence static and dynamic Naïve Bayes 99%
5 Wi-Fi sensing [32] Walk, Sit, Stand, Run SVM and LSTM 95%
6 SDR [33] Standing up or sitting down RF 96.70%
7 UWB [34] Standing, sitting, lying RF 95.6%
8 Wi-Fi sensing [35] Gestures FDTW 97.28%
9 Wi-Fi sensing [36] Human motion HMM 94.2%
10 Wi-Fi sensing [37] Eating Soft decision-based learning 95%
11 SDR [38] Gestures Pattern-matching 94%
12 SDR and Wi-Fi sensing [39] Hearing ML 91%
13 Radar sensing [40] Sit-to-stand, stand-to-sit, walking and jogging K-mean 85%
14 Wi-Fi sensing [41] Gestures CNN 94.45%,
15 Radar sensing [42] Walking, running, and crawling KNN 93%
16 Wi-Fi sensing [43] Whole-body movements and partial-body, seated activities ML 94.82%
17 Wi-Fi sensing [44] Upper, Lower and whole body CNN 90%
18 Wi-Fi sensing [45] Bend, hand clap, walk, phone call, sit down and squat SVM 98.4%
19 Wi-Fi sensing [46] Pick up, walking, jogging and sitting on chair Deep auto-encoder 91.1%
20 Radar sensing [47] Kitchen activities CNN 92.8%
21 Wi-Fi sensing [48] Standing and stand-up, sitting and sit down Soft-max regression 97.5%
22 Wi-Fi sensing [49] Walk, stand, empty and sit down RNN 90%
23 Wi-Fi sensing [50] Moving area, path walking Path matching 90.83%
24 Wi-Fi sensing [51] Sleep K-NN 93.88%
25 Wi-Fi sensing [52] Quantifying running SSF 93.18%
26 Wi-Fi sensing [53] Walking STFT 96%
Table 4. Summary of existing contributions in diagnosis of symptoms and monitoring of health.
1 RF sensing [55] Asthma attacks SVM 90%
2 Wi-Fi sensing [56] Post-surgical fall SVM 90%
3 Wi-Fi sensing [57] Huntington's disease SVM and RF 98%
4 Microwave spectrum sensing [58] Parkinsonian gait SVM 94%
5 Wi-Fi sensing [59] Dementia SVM 90%
6 SDR [60] Breathing rate, tremor and falls ML 98%
7 Wi-Fi sensing [61] Eclamptic seizures SVM 95%
8 Wi-Fi sensing [62] Danger-pose SVM 96.23%
9 SDR [63] Breathing SVM 85%
10 SDR [64] REM sleep disorder SVM 90%
11 Microwave sensing [65] Cerebellar ataxia SVM 99.8%
12 Wi-Fi sensing [66] Paraparesis 1D-CNN 99.4%
13 Microwave sensing [67] Neurological disorder SVM 99%
14 Wi-Fi sensing [68] Cerebellar dysfunction SVM 91%
15 Wi-Fi sensing [69] Parkinson's disease SVM 90%
16 Wi-Fi sensing [70] Tumor SVM 90%
17 Radar sensing [71] Gait SVM 95%
18 Wi-Fi sensing [72] Breathing and heart rate patterns DTW 94%
19 Wi-Fi sensing [73] Vital sign during sleep SVM and RF 93%
20 Wi-Fi sensing [74] Parkinson's disease CNN 99.7%
21 RF sensing [75] Epileptic seizures SVM 90%
22 Wi-Fi sensing [76] Fall SVM and RF 94%
23 Wi-Fi sensing [77] Fall SVM 100%
24 Wi-Fi sensing [78] Respiration rate EWMA 93.04%
25 SDR [79] Post-surgery ankle fractured CNN 98.98%
26 SDR [80] Post-surgery spinal cord FKNN 99.6%
Table 5. Time and frequency domain statistical features.
Time Domain Features
Frequency Domain Features
Minimum M i n ( X i ) Skewness 1 N ∑ i = 1 N ( x i − u x σ ) 3 FFT F F T ( d ) = ∑ n = − N N x ( n ) e − j 2 π N n d
Maximum M a x ( X i ) Kurtosis 1 N ∑ i = 1 N ( x i − u x σ ) 4 Spectral probability p ( d ) = F F T ( d ) 2 ∑ i = − N N F F T ( i ) 2
Mean 1 N ∑ i = 1 N x i Histogram H i s t o g r a m ( x i ) Signal energy E = ∑ n = − N N | p ( d ) | 2
Variance ∑ i = 1 n ( x i − u x ) 2 Interquartile range Q 3 − Q 1 Spectrum entropy H = ∑ i = − N N p ( d ) ln ( p ( d ) )
RMS 1 N ∑ i = 1 N x i 2 Range x m a x − x m i n Frequency peak M a x ( F F T ( d ) )
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Khan, M.B.; Zhang, Z.; Li, L.; Zhao, W.; Hababi, M.A.M.A.; Yang, X.; Abbasi, Q.H. A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19. Micromachines 2020, 11, 912. https://doi.org/10.3390/mi11100912
Khan MB, Zhang Z, Li L, Zhao W, Hababi MAMA, Yang X, Abbasi QH. A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19. Micromachines. 2020; 11(10):912. https://doi.org/10.3390/mi11100912
Chicago/Turabian Style
Khan, Muhammad Bilal, Zhiya Zhang, Lin Li, Wei Zhao, Mohammed Ali Mohammed Al Hababi, Xiaodong Yang, and Qammer H. Abbasi. 2020. "A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19" Micromachines 11, no. 10: 912. https://doi.org/10.3390/mi11100912
Find Other Styles
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
For more information on the journal statistics, click here.
Multiple requests from the same IP address are counted as one view.
Zoom | Orient | As Lines | As Sticks | As Cartoon | As Surface | Previous Scene | Next Scene
Export citation file: BibTeX | EndNote | RIS
Micromachines, EISSN 2072-666X, Published by MDPI
https://www.mdpi.com/844170

redpajama_set_name: RedPajamaCommonCrawl


Discussion in 'Android Apps & Games' started by Kimstock, Sep 17, 2011.
Well its not really a game, just a graphics demo I made for my first project using unity on android. I haven't tested it on anything but my xperia play so maybe you guys could try it out and let me know, Cheers.
Thanks Junkish, I updated the post.
Graphics looks amazing but it lags like crazy on my SGS2. Is this going to be an RPG or Action/Adventure game?
cannot get passed the Unity logo on Galaxy S with CM7 gingerbread rom.
Flawless on the EVO 3D which has a similar graphics chip to the PLAY (Adreno 220 vs. 205).
Although it is just a demo, it would be nice to be able to explore the entire castle vs. the one room inside and that outside area it currently has. The jump feature is cool double tapping the right control stick, though jumping seems to be exagerated a bit (almost like you're on the moon).
Some more sounds would be nice like footsteps as you walk/jump. The right control that you use too look around could use some tuning as well (seems a little slow). Maybe it's more realistic the way it is now; I'm just used to playing FPS games with a mouse sensitivity realtivity high (10-15) and being able to turn around as slow or as fast as I please.
Good job though! I can't wait to see what else you have in the works.
Definitely runs better on SGSII with either Tegra or Power VR plugins enabled.
Looks amazing, youre making the game all by yourself?
Runs pretty smooth for me with the Qualcomm plugin.
Realy nice demo im downloading now..
Hi, I apologise for the slow performance, I worked on it all night optimising it, try it again and let me know what its like now.
@NZtechfreak - do you think its the way unity is coded? maybe the next release at the end of this month will fix things.
@Spartoi - this is just a demo using all that I have learned in unity thus far, I haven't decided what my first proper game will be yet but I thought i'd release this with Ads and it might help pay off some of the licences I had to buy to make games for android.
@androidquake - yes all by my self, although obviously I did use the Unity engine.
Yep, or a least unitys version of java script, there is some c# in there aswell for the ads. Most of it comes from examples included in unity and the unify wiki as well as other various forums/blogs around the internet. I'm what some people may refer to as a copy and paste coder.
Performance is not great and I don't think it's making any difference what plugin I use.
Looks great, runs great on my Transformer but that ad bar is horrible.
I left a comment on the Market, this is an outstanding tech demo. I have a problem on my Xoom where the ad displays as a large white square in the upper center of my screen, it says there is an error displaying it for my device.
I would very much love to see you open it up a bit more explore the entire map if possible, I'm not sure that it is or not.
Fantastic job none the less!
The video looks exciting. I gonna playing it too.
That's not my experience, it runs but the performance is not great, controls are laggy so the framerate probably isnt very high and this is with the latest firmware.
Runs silky smooth on my Evo 3D. Great job! Seeing this shows how a port of Elder Scrolls Oblivion would be possible on our phones.
Its runs a lot better on the SGS2 with the Qualcomm plugin.
Hi, thanks for the feedback, I guess different people have different opinions of what framerate/lag is acceptable.
Thanks to those of you who commented on the android market, the first one I received was a bit negative but since then its been fairly positive.
About the ad bar, it is a bit horrible isn't it, I'll do somthing about that soon, i'm not sure what exactly, probably have it come up for a few seconds and then go away, or something along those lines.
I can see a few more ways to improve performance so I think i'll do one more update with the ad fix as well before moving onto my next game. Cheers.
Kim, love the Skycastles demo and would like to get in touch directly. I have sent you an email on here but I am not sure how efficient the system is on here.
Please get in touch as I have a possible proposition for you that might be of interest.
I know this one's kind of died down but I just checked out the app and have to say those are the best graphics I've seen yet on an android game. Gorgeous. Ran 100% smooth on my Inspire. Ads are a bit much but it's easy enough to get rid of them with the wi-fi toggle.


text:
Professor Seungwoo Seo, KT-SNU hold hands to kick-off 6G going beyond 5G (Dailian, 20190602)
▲ Dong-Myun Lee, Head of Future Platform Business Group, is taking a photo with Seungwoo Seo, director of Institute of New Media and Communications, after the signing ceremony of the MoU for cooperation in 6G communication system and autonomous driving research held at the Institute of New Media and Communications (INMC) in SNU last 31st.
MoU on 6G and autonomous vehicle research
KT held hands with SNU and kicked off the development in 6G (sixth generation mobile communication) technology going beyond 5G (fifth generation mobile communication).
KT revealed on the 2nd that it signed the MoU with SNU INMC for joint research on 6G network and cooperation in the autonomous vehicle industry.
KT convergence research center and the institute will establish a consortium and commence their research. They will jointly work on the direction and standardization of 6G and cooperate on discovering new markets in the autonomous vehicle industry as well as on improving regulations.
In particular, they are planning on discovering new areas to apply autonomous driving technology leveraging hands-on industry experience that KT has and other technologies they possess such as 5G network, 5G edge cloud, Vehicle-to-Everything(V2X) communication, and 5G security solution GiGAstealth.
Dong-Myun Lee, head of KT Future Platform Business Group(president), revealed that as a company receiving global attention for its 5G technology, it will cooperate with SNU, the main source of research in Korea, for the standardization of 6G communication technology and commercialization of autonomous driving.
Hongbeom Jeon, head of convergence technology (vice president), emphasized that it will not only work for the successful launching of 5G network, but will also start leading the standardization of future 6G network.
Seungwoo Seo, director of SNU INMC (professor of department of electrical and computer engineering) said, "the MoU with KT holds significance in opening up the possibility of accelerating the commercialization of future technologies. We expect to make advancement in future communication system and its applications through cooperation with KT.


text:
Home Business Casita Del Campo Is Among a Growing List of Small L.A. Businesses...
Eric Axene
L.A. History
Casita Del Campo Is Among a Growing List of Small L.A. Businesses Struggling to Survive
Small businesses are the soul of the city, but many are facing extinction amid the pandemic
In late July, we chronicled ten local businesses that were struggling to find their footing amid the pandemic. Here are six more L.A. institutions that are in danger of closing. All photos by Eric Axene. As told to Chris Nichols.
Bowers & Sons
Vivian Bowers, Owner and Operator
My grandparents came to Los Angeles for better opportunities in 1946. They opened a dry cleaners in Westlake and my parents met working in the shop. My father moved it to Central Avenue in 1964 and bought the whole block of storefronts. We are in South Central and while it's changing, we do need our protection here. Law enforcement comes in routinely and they pay $7.11 for a two-piece uniform, while a lady might pay $20 for a nice designer dress. They got that deal because our family philosophy through the years has been that they protect and serve—and maybe they'll get it cleaned twice a week. It's been a roller coaster ride. I saw a lot of people on the streets and there was a bit of an uptick, but now it's just dead. I'm more concerned about my son's income right now. He's a hip-hop artist, not a rapper, even though he has a potty mouth. Me and my husband work his merch booth when he plays concerts and that's where you make your money. We're both very much in survival mode. I let all my kids know if you need me, I'm here.
Green Float Company
Wendy Hart, Designer/Coordinator
My husband's family started this 99 years ago. We make parade floats from scratch. It can be really physical; they're 14 feet of wood and iron and hard to wrangle.
We start the year with Martin Luther King parade, then the Golden Dragon parade in Chinatown, the Bangladeshi community parade, the Strawberry festival, we do the Pride parades, which get a little flamboyant. We did one for Frederick's of Hollywood that was hot pink and silver and had the Temptations riding on it. People really remember that one.
I don't know when we'll be able to have parades again, large gatherings could be completely taken away from us. We have a warehouse full of floats, props, and machinery. I don't see us keeping it.
Capoeira Brasil
Jessica de Lima-Moran, Capoeirista and Owner
We have been capoeristas for over 20 years. Sean has worked as an actor since he was a kid and I in many fields including marketing and even in the City Hall office of gang reduction.
Our passion has always been the art of Capoeira. We've taught in L.A. for years, and on March 15 took a huge step, signing a five-year lease on our own space. Fifteen days later we had to close. There is a strong family vibe at our gym, we've been doing Zoom classes to sustain our membership. It works for some and not for others. There is considerable Zoom fatigue!
Casita del Campo
Robert del Campo, Owner
There is no time to think about crying or despair. We pivoted on that first day, but we never shut down. As soon as the ABC allowed margaritas to be delivered, the to-go orders started to skyrocket.
My dad was a professional dancer in West Side Story and An American in Paris before he opened the restaurant. You don't get rich in the arts. You do it for the passion you have for the craft. We're turning the Cavern Club into a dinner theater with barriers between each table. The performers want to come back so badly. Jackie Beat can't do her one-woman show without an audience to riff off of. If we're going to have to shut down, I'm going to go out swinging. This place is a legacy and a community anchor. Our goal is to be here another 58 years.
Pacific Harps
Carolyn Sykes, Harpist, Harp Teacher, and Sales Manager
I am originally from Australia. when I was 3, I told my parents I wanted a harp. They said, "That's just the biggest, goldest thing you've ever seen," but I nagged until I was 16 and they got me one. I came to L.A. to audition for Cal Arts and won the Harpo Marx Scholarship. Because of COVID I've been doing virtual showroom tours and selling online, but the price of shipping tripled overnight because it's based on empty space in airplanes and flights have been drastically reduced. I also lost the summer wedding season. That's huge for harpists and there's nothing to replace that. I can't continue like this. I don't like to think about what's going to happen after this shipment. A day of reckoning is coming.
Nelson's Drug Store
Stan Lee, Owner and Head Pharmacist
Since we're an essential business we elected to open only the pharmacy and keep our gift shop closed. Without the girls that run the gift shop we would be on the verge of going out of business. We make more on a Hallmark card than we do on a prescription.
The store's been open since 1936 and my dad came here in 1950. I've been here 45 years and my son is working here now. My other son works for CVS but he'd rather be here. I had a heart transplant and I've been doing fine but I'm supposed to be the most at risk. Every once in a while, somebody will walk in without a mask. One said, "This is a free country!" so rather than antagonize him further I got him and his really important purchase of mustache wax in and out as quickly as possible. I haven't had to Taze anyone yet.
RELATED: Some of L.A.'s Most Beloved Institutions Are Threatened with Extinction
Previous articleMorning Brief: On Day One of the RNC, 27 Former GOP Lawmakers Back Biden
Next articleFacebook Cracks Down on Some of the Biggest QAnon Accounts
Spread Some Local Love with Los Angeles Magazine
Some L.A. Ralphs Stores Briefly Administered Vaccines to People Who Aren't Yet Eligible


If object has bottom consonant, 을 (eul) comes after object.
If object has no bottom consonant, 를 (leul) comes after object.
I met a friend. – 나는 친구를 만났습니다.
I ate dinner with my friend. – 나는 친구와 저녁을 먹었습니다.
Possesive Particle is like My, Your, Our, 's etc.
My friend came to America. – 나의 친구가 미국에 왔습니다.
My Brother's bag is red. – 나의 동생의 가방은 빨간색입니다.
와(wa) comes if the word in front of it has no bottom consonant.
과(gwa) comes it the word in front on it has bottom consonant.
I went to baseball park with my friends.- 나는 친구들과 야구장에 갔습니다.
why the particle 를 is transcribed as leul, since it's never pronounced like this? ㄹsound between vowels is pronounced as "r".
Anyway, keep up the good work!!
In Korean, there actually isn't any "r" pronunciations. I know it's confusing. I think some Romanization standards use r instead of l. I use "l" for all ㄹ.
My understanding is that in this instance it's 'leul', but the pronounciation is nearer to an English 'r' when it appears at the start of a set of characters.
For instance, the '라' in 라면 sounds more like 'ra' than for instance '열' which is more like a pure L as it would be in English. So if a syllable starts with a ㄹ it's more like an r (but different), and if it ends its basically an L. Try some combinations with NeoSpeech.
스티브와 나는 같은 학교에 다닙니다.
같은- are you able to use the subject marker/topic marker twice in a sentence? if so could you explain. and does wa and gwa connect to nouns in a sentence?
another way of saying 나의 is 내. right?
and another way of saying 너의 is 네 right? im not sure. can someone tell me.