A Passive Estimator of Functional Degradation in Power Mobility Device Users

Publication Type:
Conference Proceeding
Proceedings of the IEEE/RAS-EMBS International Conference on Rehabilitation Robots, 2015, pp. 997 - 1002
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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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