Holloway, C;
Heravi, B;
Barbareschi, G;
Nicholson, S;
Hailes, S;
(2017)
Street Rehab: Linking Accessibility and Rehabilitation.
In:
Engineering in Medicine and Biology Society (EMBC). 2016 IEEE 38th Annual International Conference of the.
(pp. pp. 3151-3154).
IEEE
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Abstract
As part of the Accessible Routes from Crowdsourced Cloud Services project (ARCCS) we conducted a series of experiments using the ARCCS sensor to identify push style of wheelchair users. The aim of ARCCS is to make use of a set of well-calibrated sensors to establish a processing chain that then provides ground truth of known accuracy about location, the nature of the environment, and physiological effort. In this paper we focus on two classification problems 1) The push style employed by people as they push themselves and 2) Whether the person is being pushed by an attendant or pushing themselves (independent of push style). Solving the first enables us to develop a level of granularity to pushing classification which transcends rehabilitation and accessibility. The first problem was solved using a wrist-mounted ARCCS sensor, and the second using a wheel-mounted ARCCS sensor. Push styles were classified between semi-circular and arc styles in both indoor and outdoor environments with a high-decrees of precision and recall (>95%). The ARCCS sensor also proved capable of discerning attendant from self-propulsion with near perfect accuracy and recall, without the need for a body-worn sensor.
| Type: | Proceedings paper |
|---|---|
| Title: | Street Rehab: Linking Accessibility and Rehabilitation |
| Event: | 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
| Dates: | 16 August 2016 - 20 August 2016 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/EMBC.2016.7591401 |
| Publisher version: | https://doi.org/10.1109/EMBC.2016.7591401 |
| Language: | English |
| Additional information: | Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Keywords: | Wheelchairs, Support vector machines, Niobium, Wheels, Accelerometers, Injuries, Data visualization, wrist mounted ARCCS sensor, street rehab, accessibility, rehabilitation, Accessible Routes from Crowdsourced Cloud Services project, wheelchair users |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1537289 |
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