User Intent Identification in a Lower-Extremity Exoskeleton via the Mahalanobis Distance

Abstract

Existing strategies for controlling lower-limb robotic exoskeletons place different emphasis on the user's intentions considered at various resolutions, from high-level goals (increase speed) to mid-level actions (increase stride length) to low-level joint behaviors (increase hip flexion). While sensors onboard the exoskeleton sense the human only indirectly, via the human-robot interface, they offer advantages over more direct methods in terms of the time required to don the device. In this study, exoskeleton users, both able-bodied and having spinal cord injury, were asked to perform changes in their intended gait speed. Onboard sensor measurements were used offline to test an intent identification algorithm based on the Mahalanobis distance. The algorithm's goal is to identify an intent change and correctly classify its type, but not to realize that change via the exoskeleton. The algorithm correctly identified instances in which the user desired to walk faster or slower than the nominal speed in the device. For able-bodied subjects, the average delay between the known intent change and correct identification by the algorithm was 0.63 s. This delay for non-able-bodied subjects was 0.93 s on average. These proof-of-concept results show that intent identification based on the Mahalanobis distance is possible, while analysis of the approach suggests areas for further improvement.