Human Activity Recognition for an Intelligent Knee Orthosis

This paper investigates the possibility to classify isolated human activities from biosignal sensors integrated into a knee orthosis. An intelligent orthosis that is capable to recognize its wearers activity would be able to adapt itself to the users situation for enhanced comfort. We use a setup with three modalities: accelerometry, electromyography and goniometry to measure leg motion and muscle activity of the wearer. We segment signals in motion primitives and apply Hidden Markov Models to classify these isolated motion primitives. We discriminate between seven activities like for example walking stairs and ascend or descend a hill. In a small user study we reach an average person-dependent accuracy of 98% and a person-independent accuracy