A passive estimator of functional degradation in power mobility device users

Poon, J; Miro, JV; Black, R

A passive estimator of functional degradation in power mobility device users

Poon, J Miro, JV

Black, R

Permalink

Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Rehabilitation Robotics, 2015, 2015-September pp. 997 - 1002
Issue Date:: 2015-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (837.93 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Poon, J	en_US
dc.contributor.author	Miro, JV https://orcid.org/0000-0002-0083-7797	en_US
dc.contributor.author	Black, R	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	IEEE International Conference on Rehabilitation Robotics, 2015, 2015-September pp. 997 - 1002	en_US
dc.identifier.isbn	9781479918072	en_US
dc.identifier.issn	1945-7898	en_US
dc.identifier.uri	http://hdl.handle.net/10453/36804
dc.description.abstract	© 2015 IEEE. This paper documents the development of a passive technique for assessing a power mobility device user's driving proficiency during everyday driving activities outside formal assessment conditions by therapists. This is approached by first building a model by means of an Artificial Neural Network to infer longer-Term destinations for discretized bouts of travel, and subsequently drawing cues indicative of decline in driving proficiency for the duration of point-To-point navigation rather than relying on instantaneously calculated metrics. This resultant quantity, which we refer to as 'functional degradation', can then provide therapists with additional information concerning user health or serve as a leveraging parameter in combinatory shared-control mobility frameworks. Experiments conducted by able-bodied users subject to simulated noise scaled to varying degrees of functional degradation reveal a quantitative correlation between these longer-Term proficiency metrics and the magnitude of degradation experienced; a promising outcome that sets the scene for a larger-scale clinical trial.	en_US
dc.relation.ispartof	IEEE International Conference on Rehabilitation Robotics	en_US
dc.relation.isbasedon	10.1109/ICORR.2015.7281334	en_US
dc.title	A passive estimator of functional degradation in power mobility device users	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2015-September	en_US
utslib.for	091303 Autonomous Vehicles	en_US
dc.location.activity	Singapore
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	2015-September	en_US

Abstract:

© 2015 IEEE. This paper documents the development of a passive technique for assessing a power mobility device user's driving proficiency during everyday driving activities outside formal assessment conditions by therapists. This is approached by first building a model by means of an Artificial Neural Network to infer longer-Term destinations for discretized bouts of travel, and subsequently drawing cues indicative of decline in driving proficiency for the duration of point-To-point navigation rather than relying on instantaneously calculated metrics. This resultant quantity, which we refer to as 'functional degradation', can then provide therapists with additional information concerning user health or serve as a leveraging parameter in combinatory shared-control mobility frameworks. Experiments conducted by able-bodied users subject to simulated noise scaled to varying degrees of functional degradation reveal a quantitative correlation between these longer-Term proficiency metrics and the magnitude of degradation experienced; a promising outcome that sets the scene for a larger-scale clinical trial.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/36804