Body sensor network-based spasticity detection

Spasticity is a common disorder of the skeletal muscle with a high incidence in industrialised countries, occurring for example after multiple sclerosis or stroke and is associated with brain or spinal cord damage. A quantitative measure of spasticity using body-worn sensors is desirable in order to assess rehabilitative motor training and prevent damage, induced by the externally applied forces or torques from the movement support system. In this contribution we present a new approach to spasticity detection using the Integrated Posture and Activity NEtwork by Medit Aachen (IPANEMA) body sensor network (BSN). Towards this goal, a new electromyography (EMG)-sensor node is developed and employed in human locomotion. Following an analysis of clinical walking data of hemiplegic patients, a novel algorithm is developed based on the idea to detect co-activation of antagonistic muscle groups as observed in the exaggerated stretch reflex with associated limb or joint rigidity. The algorithm is subsequently employed in real walking tests conducted with the IPANEMA BSN showing good detection performance. We furthermore introduce a measure for the spasticity severity based on energy considerations of the recorded EMG signals.