Biorealistic Control of Hand Prosthesis Augments Functional Performance of Individuals With Amputation

The human hand has compliant properties arising from muscle biomechanics and neural reflexes, which are absent in conventional prosthetic hands. We recently proved the feasibility to restore neuromuscular reflex control (NRC) to prosthetic hands using real-time computing neuromorphic chips. Here we show that restored NRC augments the ability of individuals with forearm amputation to complete grasping tasks, including standard Box and Blocks Test (BBT), Golf Balls Test (GBT), and Potato Chips Test (PCT). The latter two were more challenging, but novel to prosthesis tests. Performance of a biorealistic controller (BC) with restored NRC was compared to that of a proportional linear feedback (PLF) controller. Eleven individuals with forearm amputation were divided into two groups: one with experience of myocontrol of a prosthetic hand and another without any. Controller performances were evaluated by success rate, failure (drop/break) rate in each grasping task. In controller property tests, biorealistic control achieved a better compliant property with a 23.2% wider range of stiffness adjustment than that of PLF control. In functional grasping tests, participants could control prosthetic hands more rapidly and steadily with neuromuscular reflex. For participants with myocontrol experience, biorealistic control yielded 20.4, 39.4, and 195.2% improvements in BBT, GBT, and PCT, respectively, compared to PLF control. Interestingly, greater improvements were achieved by participants without any myocontrol experience for BBT, GBT, and PCT at 27.4, 48.9, and 344.3%, respectively. The functional gain of biorealistic control over conventional control was more dramatic in more difficult grasp tasks of GBT and PCT, demonstrating the advantage of NRC. Results support the hypothesis that restoring neuromuscular reflex in hand prosthesis can improve neural motor compatibility to human sensorimotor system, hence enabling individuals with amputation to perform delicate grasps that are not tested with conventional prosthetic hands.

Development of Multifunctional Myoelectric Hand Prosthesis System with Easy and Effective Mode Change Control Method Based on the Thumb Position and State

Commercial multi-degrees-of-freedom (multi-DOF) myoelectric hand prostheses can perform various hand gestures and grip motions using multiple DOFs. However, as most upper limb amputees have less than two electromyogram (EMG) signals generated at the amputation site, it is difficult to control various hand gestures and grip motions using multi-DOF myoelectric hand prostheses. This paper proposes a multifunctional myoelectric hand prosthesis system that uses only two EMG sensors while improving the convenience of upper limb amputees in everyday life. The proposed system comprises a six-DOF myoelectric hand prosthesis and an easy and effective control algorithm that enables upper limb amputees to perform various hand gestures and grip motions. More specifically, the hand prosthesis has a multi-DOF five-finger mechanism and a small controller that can be mounted inside the hand, allowing it to perform various hand gestures and grip motions. The control algorithm facilitates four grip motions and four gesture motions using the adduction and abduction positions of the thumb, the flexion and extension state of the thumb, and three EMG signals (co-contraction, flexion, and extension) generated using the two EMG sensors. Experimental results indicate that the proposed system is a versatile, flexible, and effective hand prosthesis system for upper limb amputees.