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.

The use of haptic display to refer cues sensed electronically from a prosthetic terminal device promises to improve the function of myoelectrically controlled upper limb prostheses. This promise is often evaluated in experiments involving non-amputees, though the availability of additional haptic feedback from an intact hand (auxiliary feedback) may confound attempts to use non-amputees as stand-ins for amputees. In this paper we test the influence of auxiliary haptic feedback on myoelectric control performance by introducing various grasp conditions in a compensatory tracking task. We ask non-amputees to compensate for the motion of a random signal by producing myoelectric control signals with a hard object, soft object, or no object (requiring co-contraction) in their grasp. The error signal is displayed through a squeeze band worn about the upper arm or a visual display. Our results suggest that the main difference between tracking with haptic and visual feedback is low-frequency drift, and that auxiliary feedback does not substantially influence task performance. Despite the drift, our results show that participants are able to respond to cues presented through the squeeze band in the compensatory tracking task.

Achieving dexterous volitional control of an upper-limb prosthetic device will require multimodal sensory feedback that goes beyond vision. Haptic display is well-positioned to provide this additional sensory information. Haptic display, however, includes a diverse set of modalities that encode information differently. We have begun to make a comparison between two of these modalities, force feedback spanning the elbow, and amplitude-modulated vibrotactile feedback, based on performance in a functional grasp and lift task. In randomly ordered trials, we assessed the performance of N=11 participants (8 able-bodied, 3 amputee) attempting to grasp and lift an object using an EMG controlled gripper under three feedback conditions (no feedback, vibrotactile feedback, and force feed-back), and two object weights that were undetectable by vision. Preliminary results indicate differences between able-bodied and amputee participants in coordination of grasp and lift forces. In addition, both force feedback and vibrotactile feedback contribute to significantly better task performance (fewer slips) and better adaptation following an unpredicted weight change. This suggests that the development and utilization of internal models for predictive control is more intuitive in the presence of haptic feedback.