A passive estimator of functional degradation in power mobility device users

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