Currently available prosthetic hands are capable of actuating anywhere from five to 30 degrees of freedom (DOF). However, grasp control of these devices remains unintuitive and cumbersome. To address this issue, we propose directly extracting finger commands from the neuromuscular system. Two persons with transradial amputations had bipolar electrodes implanted into regenerative peripheral nerve interfaces (RPNIs) and residual innervated muscles. The implanted electrodes recorded local electromyography with large signal amplitudes. In a series of single-day experiments, participants used a high speed movement classifier to control a virtual prosthetic hand in real-time. Both participants transitioned between 10 pseudo-randomly cued individual finger and wrist postures with an average success rate of 94.7% and trial latency of 255 ms. When the set was reduced to five grasp postures, metrics improved to 100% success and 135 ms trial latency. Performance remained stable across untrained static arm positions while supporting the weight of the prosthesis. Participants also used the high speed classifier to switch between robotic prosthetic grips and complete a functional performance assessment. These results demonstrate that pattern recognition systems can use intramuscular electrodes and RPNIs for fast and accurate prosthetic grasp control.

In the near future, upper limb prostheses may interface with peripheral nerves of amputees to help restore vital somatosensory feedback. Achieving that goal requires the transformation of forces artificially sensed in our environment into the depolarization of afferents, by delivering levels of charge adequate for signaling tactile sensory cues yet avoiding tissue damage. The objective of the work herein was to build and test a tactile afferent modulation platform engineered to transform force data input, from an artificial sensor under ramp-and-hold stimuli, into the output of discrete charge-balanced pulses to the rat's acute sural nerve thereby eliciting compound sensory neural action potentials (CSNAPs). In vivo experiments, to stimulate both tactile end organs mechanically and sural nerves electrically, helped fit the model's empirical parameters. Input-output relationships were validated by comparing CSNAPs elicited by the tactile afferent modulation platform with those of natural end organs. The results replicated the natural response where increased force magnitude increased both CSNAP firing rates and waveform amplitudes. Next steps will involve the integration of the engineered platform with a regenerative peripheral nerve interface for the evaluation of its long-term reliability.