Neuromorphic compliant control facilitates human-prosthetic performance for hand grasp functions - Anran Xie; Zhuozhi Zhang; Jie Zhang; Weidong Chen; James Patton; Ning Lan (Neuromorphic Computing and Engineering)
Current bionic hands lack the ability of fine force manipulation for grasping fragile objects due to missing human neuromuscular compliance in control. This incompatibility between prosthetic devices and the sensorimotor system has resulted in a high abandonment rate of hand prostheses. To tackle this challenge, we employed a neuromorphic modeling approach, biorealistic control, to regain human-like grasping ability. The biorealistic control restored muscle force regulation and stiffness adaptation using neuromorphic modeling of the neuromuscular reflex units, which was capable of real-time computing of model outputs. We evaluated the dexterity of the biorealistic control with a set of delicate grasp tasks that simulated varying challenging scenarios of grasping fragile objects in daily activities of life, including the box and block task, the glass box task, and the potato chip task. The performance of the biorealistic control was compared with that of proportional control. Results indicated that the biorealistic control with the compliance of the neuromuscular reflex units significantly outperformed the proportional control with more efficient grip forces, higher success rates, fewer break and drop rates. Post-task survey questionnaires revealed that the biorealistic control reduced subjective burdens of task difficulty and improved subjective confidence in control performance significantly. The outcome of the evaluation confirmed that the biorealistic control could achieve superior abilities in fine, accurate, and efficient grasp control for prosthetic users.