it is well recognized that mobility is an important factor for the social participation and quality of life of individuals . for people who live with locomotor impairments , mobility assistive devices such as manual wheelchairs , power wheelchairs ( pw ) , scooters , and other motorized vehicles can be facilitators of mobility .
the increasing prevalence of chronic disease and the changing demographics of our society will increase the use of mobility assistive devices such as pws by older adults . using
a pw is a complex task whose efficiency and safety are modulated by factors such as individual capacity , wheelchair driving skills , design and technology features of the pw , environmental considerations , and interaction effects between these factors .
although pw mobility has many potential benefits for users , pw incidents and accidents are not uncommon and their consequences can be serious . users of pws can have difficulty maintaining a supported seated posture when subjected to external forces , .
oscillatory and shock whole body vibration ( wbv ) can be induced by ordinary obstacles or surfaces that pw users encounter every day   and are often linked to speed control of the pw .
these incidents can be associated with unsafe behaviours combined with poor pw driving skills , .
pw driving skills are typically modulated by experience and joystick control   but can be improved through training , .
few objective outcome measures used in the clinical assessment of pw mobility take into account all the factors influencing pw mobility and safety .
pw driving skills are typically evaluated with performance - based measures during standardized driving tasks in controlled or real environments , and graded by an observer who will pass judgement on the individual s skill level .
insufficient research has been published on the ability of these instruments to extrapolate to pw mobility and safety in everyday use .
indeed , while pw driving skills are an important factor in the safe operation of a pw , user behaviour and risk mitigation by the user during everyday use of the pw is as important . in order to educate pw users on risk mitigation and
improve their powered wheelchair driving skills , a better characterization of pw users driving behaviour is required , in various real and ecological indoor and outdoor settings .
ambulatory real life monitoring approaches of pw use with embedded sensors and datalogging are starting to emerge .
cooper et al .   determined the driving characteristics of pw users during unrestricted community activities by using a sensor attached to a pw and a custom - built datalogger .
speed , distance traveled , and the time that each subject s personal wheelchair was being driven were recorded for 24hr / d over approximately five days for each subject .
drivers of pw were most active during the afternoon and evening hours . over the 5-day period of this study , there was little variation in the speed or distance driven per day across subjects with an average daily distance of 1.7 km .
reported pw usage using instrumentation known as the wheelchair activity monitoring instrument ( whami ) .
pw usage was logged electronically , and geo - location and interview data were used to attribute pw use to ( 1 ) in the home , ( 2 ) not in the home indoors , or ( 3 ) outdoors , over a sample of twenty - five non - ambulatory , full - time pw users .
distance wheeled , time spent wheeling , number of bouts , time spent in the wheelchair , and the percentage of time spent wheeling were measured to describe wheelchair use .
overall , most research into pw mobility using embedded sensors and datalogging have reported global metrics on use of pw , such as the total distance and time spent moving each day , and have not taken into account context specific information on pw . to improve our understanding of pw use ,
its potential interrelationship with the environment and the driving skills of users , and ultimately its impact on the mobility of older adults , better outcome measures of pw mobility need to be developed .
advances in data mining methods combined with the availability of low cost sensing technologies provide new opportunities to automatically classify pw activity , and understand behaviours of pw users in natural environments . in our previous work , we presented a method to automatically recognize and categorize five different driving activities during pw use , directly from sensor data .
the method trains a support vector machine classifier , using time - delay embedding features of sensor - based data collected from a datalogging platform installed on a pw .
the objectives of the present study are to : 1 ) test the robustness of the events recognition method with a dataset containing a larger spectrum of pw activities ( 35 instead of 5 ) , 2 ) assess its performance to automatically classify safe vs. unsafe activities using a very limited training set ( only five examples of each activity ) .
fig . 1 provides an overview of the multiple components of our end - to - end system for automatic detection and classification of pw driving activities .
first , the data - logging device handles real - time recording of sensor data onboard the pw .
second , event segmentation is applied to the recorded data to identify events of interest from the data - stream .
next , feature extraction computes a rich set of features from the events of interest . finally , we apply the machine learning classifier to categorize the events according to type ( safe vs. unsafe ) and activity ( 35 different classes ) . 
 figure 1.classification process using different features .
a proof of concept for a measurement approach to monitor real life use of pw driving was developed and is currently in use with a sample of pw users .
the platform , called the wireless inertial measurement unit with gps ( wimu - gps ) ( fig .
2 ) , consists of a datalogger with embedded sensors and external sensors installed on the pw . the wimu - gps contains a number of sensors and components . in this paper
the rationale for this choice is to limit the number of sensor inputs in order to explore the power of the proposed event detection and classification methods under a set of distinct and similar driving tasks .
accelerometers are inexpensive , reliable , and unobtrusive , thus would be easy to install on any pw . in our case , the wimu - gps is installed on the base near the wheel , which takes only a few minutes and minimal tools to install . 

once the datalogging platform has been installed on the pw ( an oasis model from orthofab ) , a wheelchair user ( in our case a research assistant ) is asked to perform 35 distinct activities , listed in table 1 .
the activities are selected so that they cover most common wheelchair activities in different speeds , directions , and other conditions , following consultation with clinicians . of the 35 activities ,
15 are identified as being safe activities , and 20 are identified as unsafe a priori .
unsafe activities are defined as those activities where collisions with objects occurred at different speeds .
the remaining activities are defined as the safe activities . the rational for the choice and segmentation of the driving tasks ( safe or unsafe )
is based on the assumptions that irrespective of the task performed , impacts ( collisions ) are detrimental to the safety of the user as they have difficulty maintaining a supported seated posture when subjected to external forces . as such , while manoeuvring an pw , collisions can occur from different point of the pw ( forward motion , backward motion , side motion ) at different speeds .
many of the safe activities selected ( such as passing a step at high speed ) can potentially be mistaken with the unsafe activities .
this is done intentionally to ensure that our event classification process leads to robust classification of activities . 

each activity is repeated 30 times ( each of these repetitions is considered an  event  ) .
the 30 events for each activity are then concatenated to form a quasi - periodic signal for each activity .
the 30 events are further separated into six cross - validation sets , each containing five repetitions of each activity .
the reason for this is two - fold : it allows us to show that the system can be trained from relatively few examples ( five events for each activity ) , and by having cross - validation sets we can get a confidence estimate on our results .
the goal of this component is to extract events - of - interest from the input signal ( without , at this stage , identifying the activity s type ) .
segmentation of the input signal into events - of - interest during long - term recordings is an essential processing step to make the proposed approach viable .
if we were to keep all the data for the classification steps , then we would face a substantial class imbalance problem , whereby the activity corresponding to standard driving ( without an event - of - interest ) would constitute a much larger portion of the dataset than the specific activities .
this often makes the classification task unnecessarily difficult for the algorithms . to segment the acquired data , accelerometer signals \documentclass[12pt]{minimal } 
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after the preprocessing step , accelerometer data points in \documentclass[12pt]{minimal } 
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clustering is achieved using the k - means clustering algorithm , which partitions data into \documentclass[12pt]{minimal } 
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one cluster is composed of events - of - interest , to be further classified , and all the subsequent steps of our methodology are applied only to the data points that fall in this cluster .
one of the core components of the system is the extraction of appropriate features on which to perform automatic classification .
we consider four different types of features , each of which focuses on different signal properties : time - delay embedding features , simple time - domain features , frequency - domain features , and wavelet transforms .
one of the contributions of this paper is to provide a detailed comparison of their performance on a challenging dataset .
the first feature set considered contains simple characteristics of the time - series : mean , variance and line length on each of the three accelerometer coordinates .
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in addition to fft coefficients , a vector of wavelet transform coefficients is also considered .
using wavelet analysis , the original signal is decomposed into coefficients containing both temporal and spectral information .
this information can be used to identify the point at which the activity type is changed .
studies have shown that wavelet coefficients extracted from accelerometer signals can provide useful information for activity analysis , .
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this structure is useful for fast searching because it organizes the data points spatially , and can respond to pairwise distance queries in time linear in the number of points and in the dimension of the tree . for our experiments
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 } { } \begin{align}&\hspace { -1.5pc}\min \nolimits _ { \mathrm { \omega , b , \zeta } } { \frac { 1}{2}\mathrm { \omega } ^{\mathrm { t}}\mathrm { \omega + c}\sum \nolimits _ { \mathrm { i=1}}^{\mathrm { n } } \mathrm { \zeta } _ { \mathrm { i } } } \notag \\&\hspace { -1.5pc}\mathrm { subject~ to } ~\mathrm { \gamma } _ { \mathrm { i } } ( \mathrm { \omega } ^{\mathrm { t}}\mathrm { \phi } ( \mathrm { f}_{\mathrm { i } } ) \mathrm { \,+ b } ) \mathrm ~{\ge \ ,
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we begin by considering the segmentation of events - of - interest from the datastream .
, we considered datapoints at the full time resolution ( 250 hz ) . the magnitude signal \documentclass[12pt]{minimal } 
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 } { } $ k=1500 $ 
 \end{document } points , selected by cross - validation ) . for most activities , we correctly segmented the 30 repetitions of each activity .
3a presents a sample segmentation , based on 30 repetitions of the dp1l ( rolling down 1 inch slope at low speed ) activity .
for a few of the activities , some of the events were segmented into two parts .
in particular , for three of the activities ( mp1l , mp1n , mp1r ) , each event - of - interest was segmented into two separate events , rather than a single one .
3b , where we observe that each event is characterized by a double - peak . for the other activity types
( excluding mp1l , mp1n , mp1r ) , out of 960 events , our approach missed one event ( 0.1% ) and over - counted 12 events ( 1% ) . 
 figure 3.(a ) activity dp1l ( rolling down 1 inch slope at low speed ) , 30 repetitions , segmented into two groups : events - of - interest ( red boxes ) and background data ( blue signal ) .
( b ) activity mp1l ( climbing up 1  slope at low speed ) , 30 repetitions , segmented similarly ; in this case the automatic segmentation yielded 60 events - of - interest .
( a ) activity dp1l ( rolling down 1 inch slope at low speed ) , 30 repetitions , segmented into two groups : events - of - interest ( red boxes ) and background data ( blue signal ) .
( b ) activity mp1l ( climbing up 1  slope at low speed ) , 30 repetitions , segmented similarly ; in this case the automatic segmentation yielded 60 events - of - interest .
the data points extracted via segmentation are further processed for feature extraction according to the four types of features outlined above .
features of the time domain , fft coefficients , and wave transforms , were extracted using the automated feature extraction and selection toolbox ( afest ) .
features for the time - delay embeddings were produced using our own code . using these features vectors , we considered two types of classification tasks : binary classification to distinguish between safe and unsafe events , and per - type classification whereby events are categorized into the 35 types listed in table 1 . in this section we present the results of the safe vs. unsafe classification task .
the results of the per - type classification task are described in the following section . to train and evaluate the classifiers ,
the data corresponding to the events - of - interest were divided into two datasets : a training set that was used to train the algorithms , and included five trials ( out of 30 ) for each activity ; and a test set that was used to evaluate the performance of the algorithm , and included five different trials .
we used a small training set as this is more realistic for eventual deployments where assessment of pw driving skills for a given pw user could include tasks needed to generate this training set .
all reported results are the average of a \documentclass[12pt]{minimal } 
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feature extraction , with all types of features , was done over windows of 1.6 sec , assuming a 1.596 sec ( = one data point ) overlap with the preceding window .
the average classification error to detect safe and unsafe activities using each type of features is shown in fig .
we also examined the classification accuracy for each of the 35 different activity classes separately ; results are presented in fig .
figure 4.average error rate ( % ) over all safe / unsafe events , using five different types of features : time - delay embeddings , time - based features , fft coefficient magnitudes , wavelet transform , and all features .
error is defined as the number of false positives and false negatives over the total number of examples .
figure 5.safe vs. unsafe classification average error rate ( % ) per activity class , using 5 different types of features : time - based features , fft coefficient magnitudes , time - delay embeddings , wavelet transforms , and all features .
the average error rate for each event was calculated over six folds of cross - validation . in each fold , the training set included five examples of each activity .
average error rate ( % ) over all safe / unsafe events , using five different types of features : time - delay embeddings , time - based features , fft coefficient magnitudes , wavelet transform , and all features .
error is defined as the number of false positives and false negatives over the total number of examples .
safe vs. unsafe classification average error rate ( % ) per activity class , using 5 different types of features : time - based features , fft coefficient magnitudes , time - delay embeddings , wavelet transforms , and all features .
the average error rate for each event was calculated over six folds of cross - validation . in each fold , the training set included five examples of each activity . using only simple time - domain features ,
our classification algorithm incorrectly classified 9% of unsafe events as being safe , and 17% of safe events were classified as unsafe , as shown in fig .
5 that two out of 20 unsafe activities were classified as being safe more than 20% of the time . using only fft coefficients , results in fig . 4 and fig . 5
show that we can achieve a low classification error for unsafe events ( 3% ) .
the fft features also achieved the lowest error ( 33% ) on the challenging ifmr ( frontal collision with a moving object at high speed ) activity .
however the safe dp1l ( rolling down 1 inch slope at low speed ) activity was always misclassified as being unsafe .
4 , detection of events with time - delay embeddings misclassified 7% of unsafe events and 15% of safe events . the most difficult unsafe activity to classify was once again ifmr ( frontal collision with a moving object at high speed ) . of the safe activities ,
the tde features were the only ones to successfully classify dp1l ( rolling down 1 inch slope at low speed ) , with only 3% error .
5 that classification errors for unsafe activities were localized to certain activity types , for example ifcr ( frontal collision with a soft object at high speed ) and ifmr ( frontal collision with a moving object at high speed ) for the unsafe activities . finally , we considered the classification accuracy that can be achieved when combining all feature families within a single classifier . as shown in fig .
4 , with this combination , our classification algorithm achieved a misclassification rate of 2% for unsafe events and 12% for safe events .
this was lower than with any single feature family , but not significantly better than with the fft features .
this may be due to the small size of the training set ; in general , larger training sets are necessary when training classifiers with greater numbers of parameters ( features in this case ) .
this classifier outperformed all the others on most activities , though there were a number of activities for which the time - delay embedding features or fft features performed better , notably ifcr ( frontal collision with a soft object at high speed ) . running paired t - tests comparing the misclassification rates obtained with the various feature sets confirmed that we could partition feature families into two sets : { fft features , all features } vs. { time - based features , wavelet features}. results obtained with any of the feature types in the first set were significantly better ( at the \documentclass[12pt]{minimal } 
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 0.05 level ) than those obtained with any of the features in the second set , but not significantly better than those obtained within their set .
there was no significance between time - delay embeddings and any of the other feature families .
we end this section by presenting results showing the impact of the training set size on the safe / unsafe classification performance .
we focus on the fft family of features and the allfeatures set , which performed best in the results above .
that the safe / unsafe classification performance improved steadily until the 5 first training examples , and continued to improve but at a slower rate , up to 15 training examples for fft features . in the case of the allfeatures
, improvements continued up to 25 training examples ( the limit of our dataset , preserving a five example test ) and may improve further with more data .
this confirms that moderate amounts of data can be sufficient to correctly classify events , but that more data can be useful , especially when considering a greater number of features . 

the paper presents a machine learning approach targeting the development of automated analysis tools to characterize the driving behavior of wheelchair users .
the results presented constitute a proof - of - concept that the system can accurately detect unsafe activities using a variety of features and support - vector machine classification , with less than 2% error , and a relatively low rate of false positives ( 12% )
. this approach can be helpful in monitoring safe usage of the wheelchair under varied operating conditions .
the number of false alarms can likely be reduced by leveraging richer sensor data ( e.g. , using audio or video data to better diagnose activities ) . offering more training examples in a diversity of safe activities
we compared classification performance using different sets of features common in the time - series analysis literature : time - delay embeddings , time - domain characterization , frequency - domain features , and wavelet transforms .
this was deemed necessary because these features have been often used in the literature , without reliable comparison between them .
the results presented here show that the most robust performance was achieved when combining all features , however nearly as good performance was obtained by using fft features alone .
our approach could be helpful in diagnosing frequent driving mistakes in every day usage , without requiring a user to keep a detailed diary .
one of the challenges in moving forward with the implementation of this technique onboard regular pws is to validate the method using natural recording conditions .
the labels could however be assigned through post - processing of the recorded data , and corroboration through camera data .
furthermore , the current system was evaluated using activity recordings from a single user ; we expect results to be relatively robust to different users given the very narrow class of events considered , however this remains to be tested .
 using a powered wheelchair ( pw ) is a complex task requiring advanced perceptual and motor control skills . 
 unfortunately , pw incidents and accidents are not uncommon and their consequences can be serious . 
 the objective of this paper is to develop technological tools that can be used to characterize a wheelchair user s driving behavior under various settings . in the experiments conducted , 
 pws are outfitted with a datalogging platform that records , in real - time , the 3-d acceleration of the pw . 
 data collection was conducted over 35 different activities , designed to capture a spectrum of pw driving events performed at different speeds ( collisions with fixed or moving objects , rolling on incline plane , and rolling across multiple types obstacles ) . 
 the data was processed using time - series analysis and data mining techniques , to automatically detect and identify the different events . 
 we compared the classification accuracy using four different types of time - series features : 1 ) time - delay embeddings ; 2 ) time - domain characterization ; 3 ) frequency - domain features ; and 4 ) wavelet transforms . in the analysis , we compared the classification accuracy obtained when distinguishing between safe and unsafe events during each of the 35 different activities . 
 for the purposes of this study , unsafe events were defined as activities containing collisions against objects at different speed , and the remainder were defined as safe events . 
 we were able to accurately detect 98% of unsafe events , with a low ( 12% ) false positive rate , using only five examples of each activity . 
 this proof - of - concept study shows that the proposed approach has the potential of capturing , based on limited input from embedded sensors , contextual information on pw use , and of automatically characterizing a user s pw driving behavior . 
Introduction
Methods and Procedures
Results
Conclusion