Effort Estimation in Robot-aided Training with a Neural Network

Abstract

Robotic exoskeletons open up promising interventions during post-stroke rehabilitation by assisting individuals with sensorimotor impairments to complete therapy tasks. These devices have the ability to provide variable assistance tailored to individual-specific needs and, additionally, can measure several parameters associated with the movement execution. Metrics representative of movement quality are important to guide individualized treatment. While robots can provide data with high resolution, robustness, and consistency, the delineation of the human contribution in the presence of the kinematic guidance introduced by the robotic assistance is a significant challenge. In this paper, we propose a method for assessing voluntary effort from an individual fitted in an upper-body exoskeleton called Harmony. The method separates the active torques generated by the wearer from the effects caused by unmodeled dynamics and passive neuromuscular properties and involuntary forces. Preliminary results show that the effort estimated using the proposed method is consistent with the effort associated with muscle activity and is also sensitive to different levels, indicating that it can reliably evaluate user’s contribution to movement. This method has the potential to serve as a high resolution assessment tool to monitor progress of movement quality throughout the treatment and evaluate motor recovery.