Classifying wheelchair propulsion patterns with a wrist mounted accelerometer

Authors:
Brian French;Asim Smailagic;Dan Siewiorek;Vishnu Ambur;Divya Tyamagundlu
Affiliations:
Carnegie Mellon University, Pittsburgh PA;Carnegie Mellon University, Pittsburgh PA;Carnegie Mellon University, Pittsburgh PA;Georgia Institute of Technology, Atlanta, Georgia;Carnegie Mellon University, Pittsburgh PA
Venue:
BodyNets '08 Proceedings of the ICST 3rd international conference on Body area networks
Year:
2008

Citing 3
Cited 0

Machine Learning

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Abstract

In this paper, we describe a manual wheelchair propulsion classification system which recognizes different patterns using a wrist mounted accelerometer. Four distinct propulsion patterns have been identified in a limited user study. This study is the first attempt at classifying wheelchair propulsion patterns using low-fidelity, body-worn sensors. Data was collected using all four propulsion patterns on a variety of surface types. The results of two machine learning algorithms are compared. Accuracies of over 90% were achievable even with a simple classifier such as k-Nearest Neighbor (kNN). Being able to identify current propulsion patterns and provide real-time feedback to novice and expert wheelchair users is potentially useful in preventing future repetitive use injuries.