Offline and Real-Time Implementation of a Terrain Classification Pipeline for Pushrim-Activated Power-Assisted Wheelchairs
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Date
2021
Publication Type
Conference Paper
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yes
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Abstract
Pushrim-activated power-assisted wheelchairs (PAPAWs) are assistive technologies that provide propulsion assist to wheelchair users and enable access to various indoor and outdoor terrains. Therefore, it is beneficial to use PAPAW controllers that adapt to different terrain conditions. To achieve this objective, terrain classification techniques can be used as an integral part of the control architecture. Previously, the feasibility of using learning-based terrain classification models was investigated for offline applications. In this paper, we examine the effects of three model parameters (i.e., feature characteristics, terrain types, and the length of data segments) on offline and real-time classification accuracy. Our findings revealed that Random Forest classifiers are computationally efficient and can be used effectively for real-time terrain classification. These classifiers have the highest performance accuracy when used with a combination of time- and frequency-domain features. Additionally, we found that increasing the number of data points used for terrain estimation improves the prediction accuracy. Finally, our results revealed that classification accuracy can be improved by considering terrains with similar characteristics under one umbrella group. These findings can contribute to the development of real-time adaptive controllers that enhance PAPAW usability on different terrains.
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Publication status
published
Editor
Book title
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
Journal / series
Volume
Pages / Article No.
4542 - 4545
Publisher
IEEE
Event
43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2021)