Muscle-Targeted Robotic Assistive Control Using Musculoskeletal Model of the Lower Limb

Abstract

Conventional assistive and rehabilitative robotic systems often overlook human biomechanics, particularly muscular forces, as they predominantly operate in joint or task space and focus on position and exchanged forces. Similarly, traditional manual rehabilitation techniques employed by physiotherapists struggle to obtain quantitative measurements and make precise modifications to key human variables, resulting in predominantly qualitative methods and outcomes. In response to these limitations, this article introduces an innovative assistive robot controller that operates in the muscular space, targeting specific muscles in the lower limb, and distinguishing itself from existing solutions that focus primarily on joint or task space. A key innovation of our approach is the real-time measurement of muscular forces during dynamic tasks, obtained from a calibrated musculoskeletal model. These measurements enable the establishment of a multistep closed-loop controller, with the outer loop precisely tracking the desired muscular forces. Implemented within a configurable viscous environment, the controller provides a natural response for the user. Experimental evaluations conducted using a parallel robot designed for rehabilitation demonstrate the controller's efficacy. Incorporating the outer loop reduced the median relative error of the tracked muscular force by nearly 80% and decreased the variability of this error by over 85% compared to a pure viscous environment defined as the baseline. These findings highlight the potential applications of this control framework in areas, such as assistive robotics and precision rehabilitation. By achieving objective measurement and control, the system may enhance rehabilitation outcomes, offering tailored exercises that match the individual needs, capabilities, and engagement of each patient.