DocumentCode :
19236
Title :
Automatic Detection and Classification of Unsafe Events During Power Wheelchair Use
Author :
Pineau, Joelle ; Moghaddam, Athena K. ; Hiu Kim Yuen ; Archambault, Philippe S. ; Routhier, Francois ; Michaud, Francois ; Boissy, Patrick
Author_Institution :
Sch. of Comput. Sci., McGill Univ., Montreal, QC, Canada
Volume :
2
fYear :
2014
fDate :
2014
Firstpage :
1
Lastpage :
9
Abstract :
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.
Keywords :
data loggers; handicapped aids; medical control systems; time series; time-frequency analysis; wavelet transforms; wheelchairs; 3D acceleration; automatic unsafe event classification; automatic unsafe event detection; data mining techniques; embedded sensors; false positive rate; frequency-domain features; power wheelchair use; time-delay embeddings; time-domain characterization; time-series analysis; wavelet transforms; wheelchair user driving behavior; Accelerometers; Educational institutions; Electronic mail; Feature extraction; Sensors; Support vector machines; Wheelchairs; Assistive technologies; accelerometers; assistive technologies; event detection; rehabilitation robotics; wheelchairs;
fLanguage :
English
Journal_Title :
Translational Engineering in Health and Medicine, IEEE Journal of
Publisher :
ieee
ISSN :
2168-2372
Type :
jour
DOI :
10.1109/JTEHM.2014.2365773
Filename :
6940270
Link To Document :
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