A passive estimator of functional degradation in power mobility device users

• Published in: IEEE International Conference on Rehabilitation Robotics pp. 997 - 1002
• Main Authors: Poon, James, Miro, Jaime Valls, Black, Ross
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.08.2015
• Subjects: Artificial neural networks; Assessments; Computer simulation; Construction; Convergence; Degradation; Devices; Driving conditions; Electric power generation; Hospitals; Mathematical models; Measurement; Monitoring; Wheelchairs
• DOI: 10.1109/ICORR.2015.7281334
• ISSN: 1945-7898

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.