title: Street rehab: Linking accessibility and rehabilitation.
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.
authors.forename: Catherine
authors.initials: C
authors.lastname: Holloway
authors.forename: Behzad
authors.initials: B
authors.lastname: Heravi
authors.forename: Giulia
authors.initials: G
authors.lastname: Barbareschi
authors.forename: Sarah
authors.initials: S
authors.lastname: Nicholson
authors.forename: Stephen
authors.initials: S
authors.lastname: Hailes
mesh_terms: D006801:Humans; D008991:Monitoring, Physiologic; D009068:Movement; D014910:Wheelchairs; D014953:Wrist / Q000502:physiology*