Minimal Sensor Setup in Lower Limb Exoskeletons for Motion Classification based on Multi-Modal Sensor Data

Abstract

Exoskeletons are considered to be a promising technology for assisting and augmenting human performance. A number of challenges related to design, intuitive control and interfaces to the human body must be addressed. In this paper, we approach the question of a minimal sensor setup for the realization of control strategies which take into account the actions currently performed by the user. To this end, we extend our previous work on online classifications of a human wearing a lower limb exoskeleton in two directions. First, we investigate the minimal number of sensors that should be attached to the exoskeleton to achieve a certain classification accuracy by investigating different sensor setups. We compare results of motion classification of 14 different daily activities such as walking forward and going upstairs using Hidden Markov Models. Second, we analyse the influence of different window sizes, as well as the classification performance of different motion types when training on multi- and single-subjects. Our results reveal that we can reduce our sensor setup significantly while achieving about the same classification performance.