Classifying Wheelchair Propulsion Patterns with a Wrist Mounted Accelerometer

Brian French; Asim Smailagic; Dan Siewiorek; Vishnu Ambur; Divya Tyamagundlu

3rd International ICST Conference on Body Area Networks

Research Article

Classifying Wheelchair Propulsion Patterns with a Wrist Mounted Accelerometer

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@INPROCEEDINGS{10.4108/ICST.BODYNETS2008.2963,
    author={Brian French and Asim Smailagic and Dan Siewiorek and Vishnu Ambur and Divya Tyamagundlu},
    title={Classifying Wheelchair Propulsion Patterns with a Wrist Mounted Accelerometer},
    proceedings={3rd International ICST Conference on Body Area Networks},
    publisher={ICST},
    proceedings_a={BODYNETS},
    year={2010},
    month={5},
    keywords={eWatch wearable sensors machine learning manual wheelchair propulsion patterns},
    doi={10.4108/ICST.BODYNETS2008.2963}
}

Brian French
Asim Smailagic
Dan Siewiorek
Vishnu Ambur
Divya Tyamagundlu
Year: 2010
Classifying Wheelchair Propulsion Patterns with a Wrist Mounted Accelerometer
BODYNETS
ICST
DOI: 10.4108/ICST.BODYNETS2008.2963

Brian French¹^,*, Asim Smailagic²^,*, Dan Siewiorek³^,*, Vishnu Ambur⁴^,*, Divya Tyamagundlu⁵^,*

1: ECE Department, Carnegie Mellon University, Pittsburgh PA 15213 +1 412 268 3372
2: ICES, Carnegie Mellon University, Pittsburgh PA 15213 +1 412 268 7863
3: HCI Institute, Carnegie Mellon University, Pittsburgh PA 15213 +1 412 268 2570
4: Department of Biomedical Eng., Georgia Institute of Technology, Atlanta, Georgia 30322 +1 404 727 9874
5: ECE Department, Carnegie Mellon University, Pittsburgh PA 15213 +1 412 596 7405

*Contact email: bfrench@ece.cmu.edu, asim@cs.cmu.edu, dps@cs.cmu.edu, gtg785q@gmail.com, divyatyam@cmu.edu

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.

Keywords: eWatch wearable sensors machine learning manual wheelchair propulsion patterns

Published: 2010-05-16
Publisher: ICST
Modified: 2010-05-16

: http://dx.doi.org/10.4108/ICST.BODYNETS2008.2963