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An Evaluation of Pulse Oximeter Modules In Capturing Photoplethysmography Signals In Order To Fabricate A Monitor To Predict Onset of Exertional Heatstroke
Anushka Sharma, Christopher Law, Choi Wan Lo, Jesse Ovia, Ming Hia, and 3 more
posted 03 Jun, 2021
During strenuous exercise, individuals face a significant risk of developing heat-related illnesses, such as exertional heatstroke (EHS). Parameters that have demonstrated an important role in monitoring and prevention of EHS development include heart rate (HR), hydration status (determined by serum electrolyte concentrations), and core body temperature. In this study, the performance of two integrated HR monitor and pulse oximeter modules (AFE4403 from Texas Instruments and MAXIM30101 from Maxim Integrated) in capturing raw photoplethysmography (PPG) signals was evaluated during resting, exercise, and motion.
PPG signals collected from a wrist-worn device using green light-emitting diodes (LED) provided good HR estimates. However, the device was not resistant to motion artefacts.
Further work was conducted to fabricate sensors that could measure sodium concentrations in sweat, and core body temperature from the skin. Once each sensor prototype is finalised, sensor integration could result in a device that measures three bodily parameters to a sufficient degree of accuracy in order th predict the onset of EHS. The fabrication of the other two sensors has not been described in this report.
The sophisticated technology available today has enabled the integration of biotechnology into modern clinical practice and has created novel opportunities for advice pertaining to overall wellbeing to be delivered through virtual platforms [1]. Elegant technologies have potentiated prospects for individuals to monitor their health in the absence of clinical supervision, by quantifying physiological parameters and relaying the appropriate information to users [2]. Refined techniques have been employed to measure parameters through wearable devices, to facilitate an extended monitoring period, and to encourage compliance [3]. A common challenge that non-invasive, user-friendly health monitoring devices face, however, is obtaining measurements with both accuracy and feasibility. Another challenge is the lack of personalisation offered by these devices.
This report proposes a multimodal monitoring device comprising three sensors, that could aid the prediction and prevention of exertional heat stroke (EHS). The device would be designed to benefit exercising individuals, for whom the development of EHS is more likely, and thus the value of continuous monitoring is more pronounced.
EHS is defined as a core body temperature exceeding 40°C, associated with neurological dysfunction [4], and is a major cause of endurance-related morbidity [5]. Milder forms of heat-related injury may predict eventual development of EHS [6]. However, due to the non-specific nature of the symptoms of this 'precursor state', and possible neurological impairment, an individual may be unaware of their increasing risk of EHS development without monitoring assistance [7]. Identifying individuals within the precursor state would provide a means for preventative intervention and could potentially extend the lifesaving "golden hour" treatment window in those identified in early stages of EHS manifestation [8].
The concept of a wearable technology designed to prevent EHS is not entirely original, and existing literature modelled our expectations when building a suitable device. Lin et al. described a device that measured HR, body temperature, environmental temperature and humidity; the device comprised a wearable component, wireless transmission and a back-end monitoring system (to analyse heatstroke risk), and employed corrective filtering algorithms [9]. Gao et al. measured concentrations of glucose, lactate, sodium and potassium in the sweat; this mechanically flexible and fully integrated sensor array for multiplex in situ perspiration was fabricated as a 'smart wristband' with a wireless, flexible printed circuit board component [10]. Wu et al. went on to include measurements of sweat pH; this was achieved by an antimony ion-selective electrode. However, the report lacked detail regarding the roles that sweat pH and skin temperature measurements play in a health-related setting [11].
Several measurable parameters were deliberated in the early stages of the research. The parameters eventually chosen included photoplethysmography (PPG), sweat sodium concentration and core body temperature. Parameters were selected on the basis that they could be measured non-invasively and are implicated in one's risk of EHS development. In this report, the evaluation of different heart rate (HR) and pulse oximetry modules is described in detail, as is an area of vast interest in research pertaining to wearable technology. The other elements to the device (measurement of sweat electrolyte concentrations and body temperature) have not been reported. PPG is a non-invasive, optical method for detecting changes in blood volume in living tissue. It allows for the instantaneous computation of both heart rate and peripheral perfusion index. EHS itself develops due to loss of the normal thermoregulatory response, exacerbation of systemic inflammatory responses, and altered expression of heat shock proteins during heat stress. This results in a range of abnormal physiological responses, summarised in Fig. 1 [12]. This pathological process involves changes in cardiac output and tissue perfusion, and thus monitoring of these processes using PPG is beneficial when assessing one's risk of EHS.
Device construction
The performance of two integrated HR monitor and pulse oximeter modules AFE4403 (Texas Instruments) and MAXIM30101 (Maxim Integrated) was evaluated using their respective evaluation modules AFE4403EVM (Texas Instruments) and MAXIM30101ACCEVKIT (Maxim Integrated) respectively. The two different sensor systems were utilised to capture the raw PPG signals. The sensor configurations used for each system are shown in Table 1.
Summary of sensor configurations used for capturing of PPG data. Separate sensor boards NJL5310R and SPH7050 from the AFE4403EVM were used to capture each of Green and Red LED channels respectively. The MAX30101ACCEVKIT contains a daughter board comprising multi-channel optical, motion and thermal sensing modules, which was configured to capture data from both Green and IR LEDs simultaneously. Abbreviations: LED – light emitting diode; PD – photodetector; NJRC – New Japan Radio Co.; IR – infrared; MEMS – micro electromechanical system.
Sensor System
Sampling Frequency
AFE4403EVM
NJL5310R (Green LED/PD; NJRC)
SPH7050 (Red LED/PD; OSRAM)
MAX30101ACCEVKIT
MAX30101 Integrated Optical Sensor (Green and IR LED/PD; Maxim Integrated)
LIS2DH (3-axis MEMS Accelerometer; ST Microelectronics)
A case for the MAX30101 sensor system was designed in SOLIDWORKS and printed using a Prusa I3 MK3 3D printer with Verbatim PET-G transparent filament. The MAX30101DBEVKIT daughter board was housed in the bottom half of the case (Fig. 2) while the USBOSMB mother board was housed in the upper half of the case (Fig. 3).
Signals from the AFE4403EVM and the MAX30101ACCEVKIT were captured using the AFE4403EVM GUI and MAX30101 EV Kit Software respectively. Prior to data acquisition, the AFE4403EVM was calibrated using the in-built calibration routine in the AFE4403EVM GUI. The various sensor positions tested are shown in Fig. 4. All data processing was carried out in MATLAB R2019a.
Signal Properties
To calculate the signal-to-noise ratio (SNR) of the collected PPG signals, the clean PPG waveform was extracted by applying a bandpass filter to detrended raw PPG signals using the MATLAB function detrend and bandpass. A frequency band of 0.5 to 4Hz was chosen, corresponding to the typical physiological range of HR [17]. The clean PPG waveform was composed of the fundamental HR component as well as its harmonics. The noise component of the waveform was obtained by applying a highpass filter using the MATLAB function highpass with a threshold frequency of 4Hz. For clarity, frequencies below 0.5Hz were not considered in the computation of SNR. The collection of data is summarised in Table 2.
PPG signals acquired under stationary and motion conditions
Type of PPG Signal
Dataset Description
PPG Signals from Stationary Position (Datasets #1–8)
The data from this dataset comprises the raw PPG signals collected from 2 healthy male human subjects of ages 21 and 24.
From Wrist
The subject was recorded while sitting with their arm rested on a table at chest height. For recordings taken from the chest, the subject was recorded while sitting with breaths regulated at 1 breath (inhalation and exhalation) per 4 second. Signals were captured for a duration of 60 seconds. For the simultaneous measurements of PPG from the wrist using the AFE4403EVM and MAXIM30101ACCEVKIT, each sensor was secured to each wrist at the position described in Fig. 6a.
From Chest
The dataset of raw PPG signals taken from the chest while stationary (Datasets #5–8) were excluded from this study as no clean PPG signals could be obtained from the data and were thus not amenable to analysis.
PPG Signals during Motion (Datasets #9–10)
The range of movements tested was adapted from a study by Zhang et al. to record signals for both periodic and non-periodic motion for a range of different arm and hand movements (Zhang et al., 2019). This included micromotions and large movements. Signals were recorded for a duration of 120 second, divided into the following segments:
1. 0-30s: Stationary
2. 31–90: Movement
3. 91–120: Stationary
The range of movements included are:
1. Index finger tapping (Datasets #9–10)
2. Running arm swing (Datasets #9–10)
3. Hand waving (horizontal) (Datasets #9–10)
4. Hand shaking (vertical) (Dataset #9)
5. Fist opening and closing (Dataset #9)
6. Radial/ulnar deviation (Dataset #9)
7. Wrist extension/flexion (Dataset #9)
The SNR is calculated by the formula:
Heart Rate Algorithm
The proposed HR estimation algorithm framework is presented in Fig. 5 and was implemented by a custom MATLAB function rory(xPPG,fsPPG). The function takes in the raw PPG time series data and the sampling rate of the sensor (in Hz), and outputs the estimated HR in beats per minute (bpm). The algorithm can be divided into 3 stages: noise removal, interval estimation, and HR estimation (Fig. 5). The discrete processing steps involved in each stage is outlined in Table 3.
Processing steps in proposed HR estimation algorithm.
BP Filter
Raw PPG signals from sensors are filtered using a bandpass filter implemented with the MATLAB function bandpass with a frequency band of 0.5 to 4Hz. The frequency band was chosen to extract a clean PPG waveform corresponding to HR between 30–240 bpm by removing random background noise and large drifts in the signal with frequencies outside this threshold.
MA Removal
Removal or correction of PPG signals corrupted by motion artefacts
Interval Estimation
Split Data into Overlapping Epochs
Filtered signal is split up into 10 second epochs using a moving window which overlap by 9 seconds
Peak Detection
For each epoch, the MATLAB function findpeaks was implemented with the threshold for 'MinPeakHeight' and 'MinPeakDistance' set at 0 and 0.4*fsPPG respectively to find the location of the peaks in the waveform
Intervals between peaks is computed by finding the distance between peaks in each epoch. The intervals are averaged to find the average interval per beat for each 10 second epoch. This average will be updated every second after the initial 10 second period.
HR Estimation
Determine Mean HR in Each Epoch
Determine Mean HR in Each Epoch: Average interval per beat for each 10 second epoch used to estimate the HR in bpm by:
The algorithm is used to calculate an estimate for HR over 10 second epochs which is updated every second to provide a continuous readout of estimated HR.
Evaluation of HR Estimation Algorithm
The HR estimation algorithm was evaluated using external databases and raw PPG data collected in this study (Table 4). The root-mean-squared error (RMSE) between the estimated HR calculated from the HR estimation algorithm and the reference HR is calculated by the following formula:
Datasets for the evaluation of the proposed HR estimation algorithm.
BIDMC Data (PhysioNet)
This dataset contains 53 recordings from critically ill patients during hospital care at the Beth Israel Deaconess Medical Centre (BIDMC), Boston, MA, USA (Pimentel et al., 2017) available from PhysioNet databank. This dataset was extracted from a larger Medical Information Mart for Intensive Care II (MIMIC-II) dataset (Johnson et al., 2016).
Wrist PPG during Exercise (PhysioNet)
This dataset contains PPG and ECG recordings from subjects performing various exercises from the PhysioNet databank (Jarchi & Casson, 2017). The HR estimation algorithm was implemented on the PPG signals from the walk and run data in the dataset. The R-R intervals (RRI) in the ECG signals from the walk and run data were calculated using an RRI estimation algorithm developed by Dr David Looney (available at https://github.com/DavidLoon/RRI_estimation) and implemented in MATLAB. The HR estimation from the ECG signals was determined using the same processing steps for Interval Estimation and HR estimation in the HR estimation algorithm. The HR estimated from the ECG signals is taken as the reference HR.
Simultaneous Stationary PPG Measurements (Dataset #11)
Simultaneous PPG measurements were taken using the AFE4403EVM and MAXIM30101ACCEVKIT following the same setup previously described for the acquisition of raw PPG while stationary. The estimated HR was computed by implementing the HR estimation algorithm on each PPG measurement.
Smartwatch HR Measurements (Datasets #12–19)
This dataset contains the performance of the HR estimation algorithm evaluated against the HR estimation capability of the Apple Watch Series 5 and the Garmin Vivoactive 3 smartwatches. Due to the proprietary nature of the HR estimation functionalities in the smartwatches, it is not known how the HR estimation is implemented. However, it is known that both smartwatches utilize an optical HR monitor with green LED/PD (Apple Inc., 2019; Garmin Ltd., n.d.). The setup for the simultaneous measurement of PPG using the AFE4403EVM/MAXIM30101ACCEVKIT and the smartwatches is the same as previously described for the acquisition of raw PPG while stationary.
PPG Measurements During Motion (Datasets #20–21)
The PPG measurements were taken from Datasets #9 and #10 and the HR estimates were obtained by implementing the HR estimation algorithm on the PPG measurements. The estimated HR was split into 3 segments corresponding to the stationary and motion segments previously described.
The SNR of the raw PPG signal was computed from 20 samples taken from a single participant for both green and red light each using the AFE4403EVM and presented in Table 5. An example of the raw PPG signal for each wavelength is shown in Fig. 6 and Fig. 7.
Comparison of SNR performance of NJL5310R (green LED/PD) and SFH7050 (red LED/PD) using the AFE4403EVM
LED/PD
Peak Wavelength (nm)
SNR (dB)
NJL5310R, Green
64.13 ± 0.94
SFH7050, Red
-8.24 ± 2.96
Comparison of SNR performance of PPG signal between green and LED/PD
The SNR of the raw PPG signals were calculated from 3 simultaneous readings obtained from a single participant using the AFE4403EVM and MAX30101ACCEVKIT with green light and summarised in Table 6. An example of the spectrogram and the frequency spectra of the signal is shown in Fig. 8.
Comparison of SNR performance of AFE4403EVM and MAX30101ACCEVKIT using green light.
Peak Wavelength(nm)
SNR ± SE (dB)
55.4707 ± 6.9066
MAX30101, Green
Comparison of SNR performance of PPG signal obtained from AFE4403EVM and MAX30101ACCEVKIT (on wrist) using green LED/PD
The SNR of 10 raw PPG waveforms was obtained from a single participant using the MAX30101ACCEVKIT housed within a custom 3D-printed case and positioned on the anterior wrist while the participant remained stationary. The results are summarised in Table 7.
SNR performance of MAX30101ACCEVKIT with case, using green light.
With Case?
Evaluation of SNR performance of PPG signal obtained using MAX30101ACCEVKIT (on wrist) with case, using green LED/PD
3 measurements of each motion type were taken using the AFE4403EVM and the MAX30101ACCEVKIT with MAX30101 for both periodic and non-periodic movements. An example of the raw PPG waveforms is provided in Fig. 9. An example of the spectrogram and the frequency spectra of the signal obtained from AFE4403EVM during periodic and non-periodic ulnar deviation is shown in Fig. 10.
The accuracy of the proposed HR estimation algorithm was tested using 53 physiological recordings of patients from BIDMC. The estimated HR was plotted against the reference HR included within the data set. Of the 53 samples, samples 40 and 41 were excluded from the calculations as the PPG and ECG recordings showed significant corruption of the signals. The correlation and agreement across all datapoints (n = 24070) contained in the 51 samples were calculated and plotted in Fig. 11.
3 simultaneous measurements were taken using the AFE4403EVM and MAX30101ACCEVKIT and the smartwatch for each condition tested (Table 8). The RMSE, standard deviation of RMSE (SD RMSE), and mean average percentage error (MAPE) for each condition is summarised in Table 8. An example of the comparison of HR estimates from our proposed algorithm and from the smartwatches is given in Fig. 12.
Average root-mean-square error (RMSE), standard deviation of RMSE (SD RMSE), and mean absolute percentage error (MAPE) of estimated HR from raw PPG measurements taken using AFE4403EVM and MAX30101ACCEVKIT from the wrist and chest compared to estimated HR from wrist-worn smartwatches.
Sensor System, LED/PD
Average RMSE
SD RMSE
AFE4403EVM, NJL5310R, Green
MAX30101ACCEVKIT, MAX30101, Green
Comparison of HR estimation algorithm against different smartwatches
The HR estimation algorithm was tested using PPG signals obtained from four subjects while running and walking on a treadmill taken from the dataset "Wrist PPG during exercise" available from the PhysioNet database. The RMSE, SD RMSE, and MAPE were calculated between the estimated HR from PPG and the reference HR calculated from simultaneous ECG measurements and summarized in Table 9. The correlation and agreement across all datapoints (n = 800) for each exercise is plotted in Fig. 13 and Fig. 14.
Average RMSE, SD RMSE, and MAPE calculated between estimated HR from PPG and reference HR from ECG.
Type of Exercise
Evaluation of HR estimation algorithm during exercise and motion
The performance of the HR estimation algorithm was also tested on the raw PPG waveforms obtained using the AFE4403EVM and MAX30101ACCEVKIT on a range of movements. The results were analysed in 3 segments corresponding to the stationary and motion segments of the PPG signal measured. A summary of the variance of HR in each segment is presented in Table 10.
Average variance of estimated HR for stationary and motion segments for difference motion types.
Stationary 1, Var (HR)
Motion, Var (HR)
Finger Tapping
Fist Clenching
Running Arm Swing
Hand Shaking
Ulnar Deviation
Hand Waving
Wrist Extension/Flexion
Non-Periodic
PPG signals generated using green light resulted in a significantly higher SNR compared to red light. This result is expected as the reflectance pulsation spectrum of blood is greatest around 575nm and smallest around 650nm. Red and infrared wavelengths of light are also known to have deeper tissue penetration, thus picks up more physiological noise such as the movement of blood or tissue beneath the sensor which are the predominant contribution to experimental noise in PPG measurements [13]. As the sensors should be able to function as part of an exercise-focused wearable device, PPG measurements using green light should be used for HR estimation.
While it has been reported that skin pigmentation affects the intensity of the reflected light in PPG measurements [13] (due to green light being more strongly absorbed by melanin compared to red light thus resulting in relatively shallower penetration into tissue [14, 15]), this was not investigated in this study.
While the AFE4403EVM shows better performance compared to MAX30101ACCEVKIT in terms of SNR, the SNR of the MAX30101ACCEVKIT was sufficiently high enough to be amenable to HR estimation. From the spectrograms and frequency spectra of the PPG obtained using the AFE4403EVM and MAX30101ACCEVKIT (Fig. 8), the fundamental frequency of the HR and its harmonics can be clearly identified from the peaks occurring between 0-5Hz and is easily distinguishable from the background noise. The periodic HR component can easily be obtained with appropriate filtering and thresholding.
Currently, it was easier to implement the MAX30101ACCEVKIT into the proposed device due to the smaller hardware size and flexible 10-pin FFC ribbon cable (compared to the DB9 to 8 pin header sensor cable for the AFE4403EVM). The MAX30101ACCEVKIT also contained an integrated accelerometer for 3-axis motion tracking, which could be used for motion artefact removal. As such, the proposed device was constructed using MAX30101ACCEVKIT as the PPG sensor.
The SNR for the MAX30101ACCEVKIT system housed within the case was found to be approximately 30% higher than without the case. This indicates that the set-up does not appear to impair signal collection and has a SNR comparable to that of the AFE4403EVM system. This increase in SNR may be due to the differences in contact pressure between the sensor and skin when securing the sensor to the wrist with and without the case. However, due to the small number of samples, it was not possible to determine the significance of this difference in SNR within this report. The effect of contact pressure on SNR could be explored in future to optimise the device design.
Voluntary motion artefacts were qualitatively observed to introduce large changes in the pulse shape, amplitude, and frequency for the ranges of periodic and non-periodic motions tested. Frequency analysis of the PPG signal obtained showed that motion artefacts resulted in noise that lied within the range of physiological frequencies (0-5Hz) and obscured the frequency peaks indicating the fundamental HR frequency. This decreased the confidence of HR estimation.
It was also observed that movements involving the wrist had a more pronounced effect on the PPG pulse waveforms than those involving the arm. Wrist motions were likely to cause greater movement of tissues beneath the wrist-worn sensor compared to arm motions, and thus introduced more physiological noise than arm movements. Alternative sensor placements could be explored in the future, such as on the chest or forehead, which are prone to less ranges of voluntary movements. The thinner layers of skin and the high impedance of the skull may also result in a high SNR for reflectance-based PPG measurements taken from the forehead.
The HR estimation algorithm described in this study closely predicted the user's actual heart rate. A strong correlation between the estimated HR and the reference HR was observed, with a Pearson coefficient of r = 0.97 and a reasonable limit of agreement of [-5.7 5.8] (Fig. 11). The estimated HR showed a slight negative bias of 0.05 bpm. The current HR estimation algorithm provides a good approximation of the actual HR when the subject remains stationary, however, the accuracy of the HR estimation is dependent on the quality (SNR, presence of MA) of the PPG signals obtained.
Furthermore, the HR estimation algorithm described in this study was comparable to smart watches. Comparison between the estimated HR using the current proposed algorithm and commercially available wrist-worn smartwatches showed that the estimated HR from the proposed algorithm was able to follow the trend of the estimated HR from the smartwatches with a small margin of error indicated by the MAPE (Table 9). The estimated HR by the Apple smartwatch appeared to be more robust against sudden large changes in HR compared to the Garmin smartwatch (Fig. 12).
HR estimated from PPG measurements taken from the chest using the AFE4403EVM were shown to have a higher percentage error against both smartwatches, while measurements taken from the chest using the MAX30101ACCEVKIT did not show large changes in percentage error compared to measurements taken from the wrist.
The HR estimation algorithm described was not robust to motion. Comparison between the estimated HR from PPG measurements against reference HR from ECG measurements in the walk and run datasets showed poor correlation and agreement, with Pearson coefficients of 0.05 and 0.13, and broad limits of agreement of [-16 47] and [-42 24] for running and walking respectively (Fig. 13 and Fig. 14). Additionally, results of the motion tests showed that the HR estimation during the motion displayed significantly larger variance compared to the stationary segments, caused by large fluctuations in the estimated HR due to motion artefacts (Table 10).
The bandpass filter implemented in the proposed algorithm can remove high frequency noise and baseline drifts from the raw PPG signal but is unable to correct motion-induced artefacts as these tend to occur within the bandpass threshold. Numerous methods can be explored for the removal of motion artefacts from a range of movements, some of which requiring additional data input such as acceleration or multichannel PPG signals (Table 11).
Current methods for motion artefact detection, correction and/or removal.
Statistical Measures
Kurtosis, Shannon Entropy
(Selvaraj et al., 2011)
Skewness, Kurtosis
(Krishnan, Natarajan & Warren, 2010)
Time-Frequency Analysis
Independent Component Analysis
(Kim & Yoo, 2006)
Empirical Mode Decomposition
(Khan et al., 2016)
Sparse Signal Reconstruction
(Zhang, Pi & Liu, 2015)
(Zhang, 2015)
Spectral Subtraction
(Zhang et al., 2019)
Wigner-Ville
(Yan, Poon & Zhang, 2005)
Wavelet Denoising
(Lee & Zhang, 2003)
(Fu, Liu & Tang, 2008)
Variable Frequency Complex Demodulation
(Dao et al., 2017)
Adaptive Filtering
(Seyedtabaii & Seyedtabaii, 2008)
Least Mean Squares Filtering
(Tanweer, Hasan & Kamboh, 2017)
Recursive Mean Squares Filtering
(Wu, Chen & Fang, 2018)
The current HR estimation algorithm is not optimised in terms of processing speed or complexity. The current algorithm recalculates the peak intervals for all peaks in each window, including redundant overlapping datapoints between adjacent windows. This could be modified in future such that the calculation of peak intervals is only performed on new data points. A different approach using spectral analysis (such as periodogram, short-time Fourier transform, continuous wavelet transform, sparse-signal reconstruction based spectral estimation) could be performed given that PPG waveforms are quasi-periodic. The current algorithm is also written for post signal acquisition analysis and will need to be modified for real-time signal analysis.
Once final prototypes of each of the aforementioned sensors are developed, the next steps pertain predominantly to integration of the sensors, and mobile application development for data display. The challenges of integration include miniaturisation, noise reduction, resilience to motion artefacts and bodily attachment of each individual sensor; over the remainder of the project, each team will work to ease the eventual integration process, whilst simultaneously designing a suitable mobile application to accompany the device.
Beats per minute
Exertional heat stroke
Light-emitting diode
Mean average percentage error
Photo plethysmography
Root-mean-squared error
Standard deviation of root-mean-squared error
Imperial College Research Ethics Committee (ICREC) is the College Ethics Committee which reviews health-related research involving human participants and/or their data that is undertaken by students. Given the nature of the research, no formal ethics approval was required in this particular case, in accordance with the guidance provided by ICREC.
No external participants were recruited for the research; therefore, there was no need to obtain consent from any third party.
All authors provided verbal and written consent for the submission of the manuscript to BMC Biomedical Engineering.
Availability of data and material
Further data can be made available upon valid request.
No authors declare any conflict of interest.
The heart rate and pulse oximeter modules were purchased by the Department of Biomedical Engineering, Imperial College London. No external funding was sourced for the remainder of the research or for publication.
All authors (Anushka Sharma, Christopher Law, Choi Wan Lo, Jesse Ovia, Ming Hia, Papavee Phongsopa, Rory Horder and Ruijin Tang) contributed equally to the construction of the research and the content of the manuscript. Anushka Sharma and Christopher Law revisited the manuscript for editing and review prior to submission.
We would like to express our gratitude to Professor Martyn Boutelle and Dr Michelle Rogers, who have guided us throughout the integration of the sensor.
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hey mimo it's my birthday. I'm 12 today and probably gonna get it as a late bday present!!!!!
Happy birthday!Mimo wishes you a great birthday, keep on commenting, and waddle on!
nice comment dal 96 the game comes out june 17th so you will have to wait about 2-3 weeks to get it.
WOO HOO! gonna go to the store and buy it!
Same! I'm getting it today too!
I know! I am getting it in 20 min! Can't wait!
Nope! I just know that I am getting it soon!
No! Cruel world... WHY?! We gotta get this person a DS stat!
That's kinda weird... Like.. a mystery! *holds out detective card* Agent Bob is on the case!
Well, some facts are pointing to a wii game! That would be awesome!
Gasp! Impersonator! *holds out detective card* I am Agent Bob, undercover! Come with me, bub!
That came out already?! Looks like I'm getting TWO games today!
LOL my sister is dying.
cool,it has a secret code that comes with it inside the case just like the first club penguin ds game.
I like the webosaur figures on the table! lawl! I just bought ModNation racers... Looks FUN!
Sorry about that, maybe if you save up your money, you can buy it on Amazon or EBay or something like that.
You know what they should have done instead of makin you do all the online missions...They should have made you type in your penguin name and password to see how many missions you have completed to see if you know about herbert already or not.
I dont have a nintendo!!!!!Do you think that the epf hq will availible for all members soon?I also hope club p will come out with a wii gane too that comes wth a code.
Yeah me too I had that idea a loooong time ago. lol what if we could connect to wifi and chat with other people using it.


The Clinical Mental Health Counseling Program at Concordia University Chicago prepares graduates with the professional knowledge, skills, and practices necessary to address the wide variety of complex issues and empower diverse populations to promote optimal wellness and growth. Graduate students receive a high quality education emphasizing Christian beliefs through integrity, competency, and leadership skills. The program also recognizes, values, and instills the development of techniques, conduct, and self-reflection imperative to effectiveness as professionals within a diverse community and world. The program requires 60 semester hours.
The Clinical Mental Health Counseling Program prepares candidates as qualified entry level counselors able to practice in a variety of contexts. These settings may include community agencies, residential settings, social service agencies, hospitals, religious organizations, and private practice. Candidates from the program exhibit high standards of professionalism while integrating theory, technique, and ethics into every aspect of their careers. The program requirements reflect current educational components of the Licensed Professional Counselor examination in the State of Illinois. The Clinical Mental Health Counseling degree also holds special and unique accreditation by the Council of Accreditation of Counseling and Related Education Programs (CACREP).
Articulate and assume the professional and ethical role of a counselor.
Discuss and demonstrate techniques of cross-cultural counseling and advocate on behalf of multicultural populations.
Describe the role that human growth and development plays in counseling interventions and modify these interventions as appropriate.
Explain theories of career development and implement career interventions.
Define and utilize counseling skills and advanced counseling interventions.
Articulate theories of group counseling and utilize leadership skills in facilitating various types of groups.
Identify and utilize various types of counseling assessments.
Describe and conduct methods of research and program evaluation.
The Masters of Arts in Clinical Mental Health Counseling is a counselor education program and is one of six degrees offered within the Division of Human Services.


On February 10, 2016, this report was posted online as an MMWR Early Release.
Zika virus is a mosquito-borne flavivirus that is related to dengue virus and transmitted primarily by Aedes aegypti mosquitoes, with humans acting as the principal amplifying host during outbreaks. Zika virus was first reported in Brazil in May 2015 (1). By February 9, 2016, local transmission of infection had been reported in 26 countries or territories in the Americas.* Infection is usually asymptomatic, and, when symptoms are present, typically results in mild and self-limited illness with symptoms including fever, rash, arthralgia, and conjunctivitis. However, a surge in the number of children born with microcephaly was noted in regions of Brazil with a high prevalence of suspected Zika virus disease cases. More than 4,700 suspected cases of microcephaly were reported from mid-2015 through January 2016, although additional investigations might eventually result in a revised lower number (2). In response, the Brazil Ministry of Health established a task force to further investigate possible connections between the virus and brain anomalies in infants (3).
Since November 2015, CDC has been developing assays for Zika virus testing in formalin-fixed, paraffin-embedded (FFPE) tissue samples. In December 2015, FFPE tissues samples from two newborns (born at 36 and 38 weeks gestation) with microcephaly who died within 20 hours of birth and two miscarriages (fetal losses at 11 and 13 weeks) were submitted to CDC, from the state of Rio Grande do Norte in Brazil, for histopathologic evaluation and laboratory testing for suspected Zika virus infection. All four mothers had clinical signs of Zika virus infection, including fever and rash, during the first trimester of pregnancy, but did not have clinical signs of active infection at the time of delivery or miscarriage. The mothers were not tested for antibodies to Zika virus. Samples included brain and other autopsy tissues from the two newborns, a placenta from one of the newborns, and products of conception from the two miscarriages.
FFPE tissues were tested by Zika virus reverse transcription-polymerase chain reaction (RT-PCR) targeting the nonstructural protein 5 and envelope genes using general methods for RT-PCR (4), and by immunohistochemistry using a mouse polyclonal anti-Zika virus antibody, using methods previously described (5). Specific specimens from all four cases were positive by RT-PCR, and sequence analysis provided further evidence of Zika virus infection, revealing highest identities with Zika virus strains isolated from Brazil during 2015. In the newborns, only brain tissue was positive by RT-PCR assays. Specimens from two of the four cases were positive by immunohistochemistry: viral antigen was noted in mononuclear cells (presumed to be glial cells and neurons within the brain) of one newborn, and within the chorionic villi from one of the miscarriages. Testing for dengue virus was negative by RT-PCR in specimens from all cases.
For both newborns, significant histopathologic changes were limited to the brain, and included parenchymal calcification, microglial nodules, gliosis, and cell degeneration and necrosis. Other autopsy tissues and placenta had no significant findings. Tests for toxoplasmosis, rubella, cytomegalovirus, herpes simplex, and HIV were negative in the two mothers who experienced miscarriages. Placental tissue from one miscarriage showed heterogeneous chorionic villi with calcification, fibrosis, perivillous fibrin deposition, and patchy intervillositis and focal villitis, while tissue from the other miscarriage had sparsely sampled normal-appearing chorionic villi.
This report describes evidence of a link between Zika virus infection and microcephaly and fetal demise through detection of viral RNA and antigens in brain tissues from infants with microcephaly and placental tissues from early miscarriages. Histopathologic findings indicate the presence of Zika virus in fetal tissues. These findings also suggest brain and early gestational placental tissue might be the preferred tissues for postmortem viral diagnosis. Nonfrozen, formalin-fixed specimens or FFPE blocks are the preferred sample type for histopathologic evaluation and immunohistochemistry, and RT-PCR can be performed on either fresh frozen or formalin-fixed specimens. To better understand the pathogenesis of Zika virus infection and associated congenital anomalies and fetal death, it is necessary to evaluate autopsy and placental tissues from additional cases, and to determine the effect of gestational age during maternal illness on fetal outcomes.
Corresponding author: Roosecelis Brasil Martines, RBrasilMartines@cdc.gov, 404-639-3886.
1Infectious Diseases Pathology Branch, Division of High-Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases (NCEZID), CDC; 2Department of Infectious Diseases, Federal University of Rio Grande do Norte, Natal, RN, Brazil; 3Department of Pathology, Federal University of Rio Grande do Norte, Natal, Brazil; 4Service for Ascertaining Death in the State of Rio Grande do Norte (SVO/RN), Natal/RN, Brazil; 5Ministry of Health, Brazil; 6Arboviral Diseases Branch, Division of Vector-Borne Diseases, NCEZID, CDC.
* Updated information about local transmission of Zika virus is available online (http://www.cdc.gov/zika/geo/index.html).
Suggested citation for this article: Martines RB, Bhatnagar J, Keating MK, et al. Notes from the Field: Evidence of Zika Virus Infection in Brain and Placental Tissues from Two Congenitally Infected Newborns and Two Fetal Losses — Brazil, 2015. MMWR Morb Mortal Wkly Rep 2016;65:159–160. DOI: http://dx.doi.org/10.15585/mmwr.mm6506e1External.


text:
Great House of Isaac Benesch and Sons
By Julie Saylor
Once a bustling department store complex on North Gay Street, the Great House of Isaac Benesch and Sons has been vacant for over a decade as the Old Town Mall waits on the progress of long stalled revitalization efforts. Isaac Benesch started his business shortly after the Civil War with a furniture store operating out of a single rowhouse. In the 1880s, as dry goods dealers like Hutzler's built their "grand emporiums" on the west side, Benesch acquired nearby rowhouses and began to rebuild them into a department store.
By 1911, his business included three large 4-story buildings, dominating the 500 block of N. Gay Street. The store at 549-557 Old Town Mall, an Italianate brick building with large windows, still features an elegant copper sign band across the facade, proclaiming the "Great House," perhaps added by Philadelphia architect Louis Levi in 1914. Next door at 565-571 North Gay Street is a four story, two bay Renaissance Revival building, of brick with terra cotta ornamentation designed by architect Charles E. Cassell and built in 1904 by William H. Porter. Cassell had a long list of major projects in Baltimore, including the grand Stewart's Department Store at Howard and Lexington Streets, built in 1900. Benesch's likely hoped Cassell could bring the same architectural magnificence to his work on the east side. More buildings went up in the 1920s with a warehouse at 600 Aisquith Street by the J.L. Robinson Construction Company, virtually unchanged from its 1925 construction.
Unlike those westside department stores, however, Isaac Benesch established an early reputation for serving all customers—black and white. One 1898 account from the Afro-American newspaper stated, "Isaac Benesch & Sons very much appreciate the large volume of colored trade which they have, coming from all parts of the city." In 1926, when few department stores hired African Americans as salesmen, Benesch hired Josh Mitchell to sell automobile tires—and featured him in advertisements. In the 1940s, the Afro-American gave Benesch an "orchid" for "serving all alike."
In the 1970s, several of the original buildings were demolished as the block was redeveloped for the pedestrian-only "Old Town Mall." The Great House had closed a few years earlier, in the early 1960s, and was run as Kaufman's Department Store until 1997.
Isaac Benesch: Photograph of Isaac Benesch. ~ Source: The Jews of Baltimore; an historical summary of their progress and status as citizens of Baltimore from early days to the year nineteen hundred and ten (1910) by Isidor Blum, Internet Archive, jewsofbaltimoreh00blum ~ Creator: John Betz
549-557 Old Town Mall, Baltimore, MD 21202
Julie Saylor, "Great House of Isaac Benesch and Sons," Explore Baltimore Heritage, accessed July 17, 2019, https://explore.baltimoreheritage.org/items/show/208.
Charles E. Cassell
Isaac Benesch
Louis Levi
Old Town Mall
Published on Feb 22, 2013. Last updated on Nov 27, 2018.
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This work is licensed by Baltimore Heritage under a Creative Commons Attribution 4.0 International License.

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: Why the datetime becomes a number? How can I make the datetime format in result? I am making the api with flask but I get a issue.
I don't know why the datetime becomes a number?
This is my code:
# -*- coding: utf-8 -*-
from flask import Flask, g, render_template, request, jsonify, json, current_app, redirect, url_for
from flask import make_response
import flask
from os.path import join, dirname, realpath
import logging
import os
import chardet
from chardet.universaldetector import UniversalDetector
import numpy as np
import pandas as pd
import sys
import tempfile

app = Flask(__name__)

@app.route('/api/tier_change',methods = ['POST'])
def index3():
    VIP_CODE = request.form['VIP_CODE']
    CHANGE_TYPE = request.form['CHANGE_TYPE']
    CHANGE_TIME = request.form['CHANGE_TIME']
    BEFORE_TIER_CODE = request.form['BEFORE_TIER_CODE']
    AFTER_TIER_CODE = request.form['AFTER_TIER_CODE']
    BEFORE_EXPIRED_DATE = request.form['BEFORE_EXPIRED_DATE']
    AFTER_EXPIRED_DATE = request.form['AFTER_EXPIRED_DATE']
    a = {'VIP_CODE': [VIP_CODE],
        'CHANGE_TYPE':[CHANGE_TYPE],
        'CHANGE_TIME':[CHANGE_TIME],
        'BEFORE_TIER_CODE':[BEFORE_TIER_CODE],
        'AFTER_TIER_CODE':[AFTER_TIER_CODE],
        'BEFORE_EXPIRED_DATE':[BEFORE_EXPIRED_DATE],
        'AFTER_EXPIRED_DATE':[AFTER_EXPIRED_DATE]}
    df = pd.DataFrame(data=a)
    df['New_date_start'] = pd.to_datetime(df['AFTER_EXPIRED_DATE'], errors='coerce', format='%Y-%m-%d')
    df['New_date_start'] = df['New_date_start'] - pd.DateOffset(years=1) - pd.DateOffset(days=1)
    result = df.to_json(orient="records")
    parsed = json.loads(result)
    if parsed:
        print(df)
        print(result)
        print(parsed)
        print(json.dumps(parsed))
        return json.dumps(parsed)
    else:
        return 'not valid'

if __name__ == '__main__':
    app.config['JSON_AS_ASCII'] = False
    app.run(debug=True, host='0.0.0.0', port=5010)

Sample input:
VIP_CODE:03K100000882
CHANGE_TYPE:Renew
BEFORE_TIER_CODE:FO
AFTER_TIER_CODE:FO
BEFORE_EXPIRED_DATE:2021-09-04
AFTER_EXPIRED_DATE:2022-09-04
CHANGE_TIME:2021-09-07

And the result:
[{"VIP_CODE":"03K100000882","CHANGE_TYPE":"Renew","CHANGE_TIME":"2021-09-07","BEFORE_TIER_CODE":"FO","AFTER_TIER_CODE":"FO","BEFORE_EXPIRED_DATE":"2021-09-04","AFTER_EXPIRED_DATE":"2022-09-04","New_date_start":1630627200000}]

The New_date_start is number. It was a date. And I have checked the dateframe.
The New_date_start is date format.
This is df:
       VIP_CODE CHANGE_TYPE CHANGE_TIME BEFORE_TIER_CODE AFTER_TIER_CODE BEFORE_EXPIRED_DATE AFTER_EXPIRED_DATE New_date_start
0  03K100000882       Renew  2021-09-07               FO              FO          2021-09-04         2022-09-04     2021-09-03

How can I fix this? And what happened in there?

A: result = df.assign(**df.select_dtypes(['datetime']).astype(str).to_dict('list')).to_json(orient="records")

redpajama_set_name: RedPajamaStackExchange


text:
<?php

namespace Orchestra\Tests\Feature\Bootstrap;

use Mockery as m;
use Orchestra\Foundation\Bootstrap\NotifyIfSafeMode;
use Orchestra\Tests\Feature\TestCase;

class NotifyIfSafeModeTest extends TestCase
{
    /** @test */
    public function it_show_notification_when_on_safe_mode()
    {
        $this->instance('orchestra.extension.status', $mode = m::mock('\Orchestra\Contracts\Extension\StatusChecker'));
        $this->instance('orchestra.messages', $messages = m::mock('\Orchestra\Contracts\Messages\MessageBag'));

        $mode->shouldReceive('is')->once()->with('safe')->andReturn(true);

        $messages->shouldReceive('extend')->once()->with(m::type('Closure'))
                ->andReturnUsing(function ($c) use ($messages) {
                    $c($messages);

                    return $messages;
                })
            ->shouldReceive('add')->once()->with('info', m::type('String'))->andReturnNull();

        $this->assertNull((new NotifyIfSafeMode())->bootstrap($this->app));
    }
}

redpajama_set_name: RedPajamaGithub