Paper Info
Title
Towards A Wearable Wheelchair Monitor: Classification Of Push Style Based On Inertial Sensors At Multiple Upper Limb Locations
Abstract
Measuring manual wheelchair activity by using wearable sensors is on the rise for rehabilitation and monitoring purposes. Stroke pattern is an important descriptor of the wheelchair user's quality of movement. This paper evaluates the capability of inertial sensors located at different upper limb locations plus wheel, to classify two types of stroke pattern for manual wheelchairs: semicircle and arc. Data was collected using bespoke inertial sensors with a wheelchair fixed to a treadmill. Classification was done with a linear SVM algorithm, and classification performance was computed for each sensor location in the upper limb, and then in combination with wheel sensor. For single sensors, forearm location had the highest accuracy (96%) followed by hand (93%) and arm (90%). For combined sensor location with wheel, best accuracy came in combination with forearm. These results set the direction towards a wearable wheelchair monitor that can offer multiple on-body locations for increased usability.
Year
DOI
Venue
2018
10.1109/SMC.2018.00266
2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
Keywords
Field
DocType
manual wheelchair, inertial sensor, push style, stroke pattern, wearable technology
Wheelchair,Computer vision,Upper limb,Computer science,Wearable computer,Usability,Forearm,Artificial intelligence,Inertial measurement unit,Treadmill,Wearable technology,Machine learning
Conference
ISSN
Citations 
PageRank 
1062-922X
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Roxana Ramirez Herrera101.01
Behzad Momahed Heravi2112.40
Giulia Barbareschi313.40
Carlson, T.4152.59
Catherine Holloway523.75