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27001 SpinSafe: An unsupervised smartphone-based wheelchair path monitoring system
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Seraj, F. and Havinga, P.J.M. and Meratnia, N. (2016) SpinSafe: An unsupervised smartphone-based wheelchair path monitoring system. In: Proceedings of the IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016, 14-18 March 2016, Sydney, Australia. pp. 1-6. IEEE Computer Society. ISBN 978-1-5090-1941-0

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Movement and social life of wheelchair users are constrained by their disability and suitability of paths they can move on. Modern electric wheelchairs offer them assisted drive, making their movement easier and longer. They, however, do not prevent accidents, injuries, and inconveniences caused by path roughness and ramp slopes. Providing information about suitability and accessibility of paths and buildings for wheelchair users will enable them to beforehand plan their trip to not to be caught by surprises or not to take a trip all together. The recent emergence of smartphones equipped with inertial sensors offers new opportunities for provision of information regarding quality and accessibility of paths and buildings for wheelchair users. To this end, we propose a smartphone-based participatory system incorporating a hybrid unsupervised machine learning technique based on Self Organized Maps (SOM) to identify path conditions and to create clusters of similar path types. Our solution provides useful information about the angle of the ramp and curb slopes as well as pavement quality and roughness and path types.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-PS: Pervasive Systems
Research Program:CTIT-General
Research Project:SMART ROAD: Participatory Sensing-based Road Monitoring Using Smart Phones
Uncontrolled Keywords:Unsupervised machine learning, anomaly detection, data analysis, signal processing, visualization, wavelet, decomposition
ID Code:27001
Deposited On:12 July 2016
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