Abstract
Stroke is a leading cause of disability worldwide, with most survivors experiencing chronic motor deficits. Myoelectric orthoses, controlled by residual muscle activity from the paretic limb, can restore upper-limb function to patients. However, existing commercial myoelectric orthoses are limited to only a single hand motion with fixed force output. In the adjacent field of myoelectric prostheses, regression algorithms have enabled simultaneous and proportional position control over multiple degrees of freedom (DOFs), which in turn has improved user dexterity. Here, we explore, for the first time, the ability to regress the kinematic position of multiple DOFs in parallel from paretic muscle activity using a Kalman filter. We collected data from seven hemiparetic patients and systematically explored the root mean squared error (RMSE) of kinematic predictions for various degrees of freedom. We show that proportional position control is possible for multiple hand and wrist motions and that unidirectional DOFs perform better than bidirectional DOFs. Using previously reported RMSEs from healthy participants as a benchmark, we found that 86% of hemiparetic patients achieved functional 2-DOF control, 57% achieved functional 3-DOF control, and 29% achieved functional 4-DOF control. Performance was similar across patient characteristics and different combinations of DOFs. This work demonstrates that multi-DOF regression is readily achievable for some hemiparetic patients. Restoring wrist motion, in addition to grasping, could have a substantial impact on the dexterity and independence of hemiparetic patients. As such, this work serves as an important first step towards multi-DOF assistive upper-limb exoskeletons.