Jiayue Liu’s research while affiliated with Shanghai Jiao Tong University and other places

Restoring neuromuscular reflex properties in the control of a prosthetic hand may potentially approach human-level grasp functions in the prosthetic hand. Previous studies have confirmed the feasibility of real-time emulation of a monosynaptic spinal reflex loop for prosthetic control [1]. This study continues to explore how well the biomimetic controller could enable the amputee to perform force-control tasks that required both strength and error-tolerance. The biomimetic controller was programmed on a neuromorphic chip for real-time emulation of reflex. The model-calculated force of finger flexor was used to drive a torque motor, which pulled a tendon that flexed prosthetic fingers. Force control ability was evaluated in a “press-without-break” task, which required participants to press a force transducer toward a target level, but never exceeding a breakage threshold. The same task was tested either with the index finger or the full hand; the performance of the biomimetic controller was compared to a proportional linear feedback (PLF) controller, and the contralateral normal hand. Data from finger pressing task in 5 amputees showed that the biomimetic controller and the PLF controller achieved 95.8% and 66.9% the performance of contralateral finger in success rate; 50.0% and 25.1% in stability of force control; 59.9% and 42.8% in information throughput; and 51.5% and 38.4% in completion time. The biomimetic controller outperformed the PLF controller in all performance indices. Similar trends were observed with full-hand grasp task. The biomimetic controller exhibited capacity and behavior closer to contralateral normal hand. Results suggest that incorporating neuromuscular reflex properties in the biomimetic controller may provide human-like capacity of force regulation, which may enhance motor performance of amputees operating a tendon-driven prosthetic hand.

Current control of prosthetic hands is ineffective when grasping deformable, irregular, or heavy objects. In humans, grasping is achieved under spinal reflexive control of the musculotendon skeletal structure, which produces a hand stiffness commensurate with the task. We hypothesize that mimicking reflex on a prosthetic hand may improve grasping performance and safety when interacting with human. Here, we present a design of compliant controller for prosthetic hand with a neuromorphic model of human reflex. The model includes 6 motoneuron pools containing 768 spiking neurons, 1 muscle spindle with 128 spiking afferents, and 1 modified Hill-type muscle. Models are implemented using neuromorphic hardware with 1 kHz real-time computing. Experimental tests showed that the prosthetic hand could sustain a 40 N load compared to 95 N for an adult. Stiffness range was adjustable from 60 to 640 N/m, about 46.6% of that of human hand. The grasping velocity could be ramped up to 14.4 cm/s, or 24% of the human peak velocity. The complaint control could switch between free movement and contact force when pressing a deformable beam. The amputee can achieve a 47% information throughput of healthy humans. Overall, the reflex-enabled prosthetic hand demonstrated the attributes of human compliant grasping with the neuromorphic model of spinal neuromuscular reflex.