Abstract

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.

Highlights

  • The loss of upper limbs hinders the ability of individuals with amputation to perform daily activities

  • As alpha motor commands were changing in real-time, the MCP joint angle and fingertip force of the prosthetic hand made changes

  • The force variability (FDRMS) of using biorealistic controller (BC) was 49.8% lower than that of proportional linear feedback (PLF) control, and the variability of MCP joint angle (JADRMS) was reduced by 19.6%

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Summary

Introduction

The loss of upper limbs hinders the ability of individuals with amputation to perform daily activities. A high percentage of individuals with amputation tend to refuse myoelectric prosthetic hands due to difficulty in control (Atkins et al, 1996; Biddiss and Chau, 2007; McFarland et al, 2010). The functionality of modern prosthetic hands is still grossly inferior compared to the dexterity of human hand. A great challenge arises when controlling prosthetic devices to interact with real-world objects that are deformable or crispy. In such cases, the prosthetic hand is expected to adapt its compliance commensurate with object stiffness as the human hand does (Balasubramanian and Santos, 2014; Zhang et al, 2021). We proposed that it is necessary to reanimate compliant property of human sensorimotor control in prosthetic hands (Lan et al, 2021)

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