An Environment Recognition and Parameterization System for Shared-Control of a Powered Lower-Limb Exoskeleton

The daily use of advanced wearable robotic devices for the assistance of people with locomotor disabilities is still facing clear limitations in usability and acceptance (e.g. cost, complexity, and inability to maintain balance). In most devices, the correct selection and initiation of pre-defined functions and activities (e.g. walking and stair ascent-descent) rely on the user's input and constant interpretation of the environment, which results in a substantial cognitive workload. In this study, a novel environment recognition and parameterization system that uses depth camera images is proposed as a potential assistant in the control of powered lower-limb exoskeletons. The feasibility of an online shared-control approach between the user and the system was assessed in two specific use-cases of lower-limb exoskeletons: Mode selection assistance and dynamic step adaptation. In a sequence of realistic daily life tasks, the assistance provided by the proposed system achieved an error below 10% with a loop frequency up to 400 Hz in terms of parameterizing the environment, and reduced the mean overall workload, measured with the Raw Task Load Index, by 19% in a group of seven neurologically intact subjects. In conclusion, an assistive environment recognition and parameterization system shows potential to reduce the cognitive workload on the user, and thereby positively influence device usability.