This study assessed the feasibility to restore finger-specific sensory feedback in transradial amputees with electrical stimulation of evoked tactile sensation (ETS). Methods: Here we investigated primary somatosensory cortical (SI) responses of ETS using Magnetoencephalography. Results: SI activations revealed a causal correlation with peripheral stimulation of projected finger regions on the stump skin. Peak latency was accountable to neural transmission from periphery to SI. Peak intensity of SI response was proportional to the strength of peripheral stimulation, manifesting a direct neural pathway from skin receptors to SI neurons. Active regions in SI at the amputated side were consistent to the finger/hand map of homunculus, forming a mirror imaging to that of the contralateral hand. With sensory feedback, amputees can recognize a pressure at prosthetic fingers as that at the homonymous lost fingers. Conclusions: Results confirmed that the direct neural pathway from periphery to SI allows effective communication of finger-specific sensory information to these amputees. INDEX TERMS Evoked tactile sensation (ETS), magnetoencephalography (MEG), prosthetic hand, sensory feedback, transcutaneous electrical nerve stimulation (TENS). IMPACT STATEMENT This study substantiated the neural basis and feasibility that electrically evoked tactile sensation can afford a non-invasive neural interface capable of restoring finger-specific sensory ability to transradial amputees.
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.
Integrating a prosthetic hand to amputees with seamless neural compatibility presents a grand challenge to neuroscientists and neural engineers for more than half century. Mimicking anatomical structure or appearance of human hand does not lead to improved neural connectivity to the sensorimotor system of amputees. The functions of modern prosthetic hands do not match the dexterity of human hand due primarily to lack of sensory awareness and compliant actuation. Lately, progress in restoring sensory feedback has marked a significant step forward in improving neural continuity of sensory information from prosthetic hands to amputees. However, little effort has been made to replicate the compliant property of biological muscle when actuating prosthetic hands. Furthermore, a full-fledged biorealistic approach to designing prosthetic hands has not been contemplated in neuroprosthetic research. In this perspective article, we advance a novel view that a prosthetic hand can be integrated harmoniously with amputees only if neural compatibility to the sensorimotor system is achieved. Our ongoing research supports that the next-generation prosthetic hand must incorporate biologically realistic actuation, sensing, and reflex functions in order to fully attain neural compatibility.
Restoring neuromuscular reflex properties in the control of a prosthetic hand may potentially approach humanlevel 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-withoutbreak" 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 tendondriven prosthetic hand.
Prosthetic and therapeutic devices have been developed to ameliorate the quality of daily living for people with amputation or neurological disorders. However, many of them fall short of functional benefits, and therefore, are frequently rejected by users due to awkward control, or no awareness of interaction during tasks. Traditional wisdom in the design of prosthetic and therapeutic devices may have emphasized the need to provide users with apparatus that replace or assist motor ability. Rather, the notion to achieve neural compatibility with the existing sensorimotor system has not been well recognized. We argue that providing biomimetic control and sensing capacity to prosthetic and therapeutic devices can enhance their neural compatibility, and therefore, can yield greater functionality in performing activities of daily lives, or in rehabilitation training. In this paper, the authors will present a range of neural technologies that may allow implementation of biomimetic sensorimotor control, including natural sensory feedback, neuromuscular like compliant control, natural module of synergy-based control, as well as advanced neural signal processing techniques. Based on the evidence in our research and in literature, we propose that achieving neural compatibility with the existing human sensorimotor system should be the ultimate goal of prosthetic and therapeutic devices.
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.
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