Background Advanced motorized prosthetic devices are currently controlled by EMG signals generated by residual muscles and recorded by surface electrodes on the skin. These surface recordings are often inconsistent and unreliable, leading to high prosthetic abandonment rates for individuals with upper limb amputation. Surface electrodes are limited because of poor skin contact, socket rotation, residual limb sweating, and their ability to only record signals from superficial muscles, whose function frequently does not relate to the intended prosthetic function. More sophisticated prosthetic devices require a stable and reliable interface between the user and robotic hand to improve upper limb prosthetic function. New Method Implantable Myoelectric Sensors (IMES®) are small electrodes intended to detect and wirelessly transmit EMG signals to an electromechanical prosthetic hand via an electromagnetic coil built into the prosthetic socket. This system is designed to simultaneously capture EMG signals from multiple residual limb muscles, allowing the natural control of multiple degrees of freedom simultaneously. Results We report the status of the first FDA-approved clinical trial of the IMES® System. This study is currently in progress, limiting reporting to only preliminary results. Comparison with Existing Methods Our first subject has reported the ability to accomplish a greater variety and complexity of tasks in his everyday life compared to what could be achieved with his previous myoelectric prosthesis. Conclusion The interim results of this study indicate the feasibility of utilizing IMES® technology to reliably sense and wirelessly transmit EMG signals from residual muscles to intuitively control a three degree-of-freedom prosthetic arm.

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A real-time system for deriving timing control for functional electrical stimulation for foot-drop correction, using peripheral nerve activity as a sensor input, was tested for reliability to investigate the potential for clinical use. The system, which was previously reported on, was tested on a hemiplegic subject instrumented with a recording cuff electrode on the Sural nerve, and a stimulation cuff electrode on the Peroneal cuff. Implanted devices enabled recording and stimulation through telelinks. An input domain was derived from the recorded electroneurogram and fed to a detection algorithm based on an adaptive logic network for controlling the stimulation timing. The reliability was tested by letting the subject wear different foot wear and walk on different surfaces than when the training data was recorded. The detection system was also evaluated several months after training. The detection system proved able to successfully detect when walking with different footwear on varying surfaces up to 374 days after training, and thereby showed great potential for being clinically useful.

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We report on our advances in sensory feedback data processing and control system design for functional electrical stimulation (FES) assisted correction of foot drop. We have applied 2 methods of signal purification on the bin integrated electroneurogram (i.e., optimized low pass filtering and wavelet denoising) before training adaptive logic networks (ALN). ALN generated stimulation control pulses, which correspond to the swing phase of the impaired leg when dorsal flexion of the foot is necessary to provide safe ground clearance. However, the obtained control signal contained sporadic stimulation spikes in the stance phase, which can collapse the subject, and infrequent broken stimulation pulses in the swing phase, which can result in unpredictable consequences. In this study, we have introduced adaptive restriction rules (ARR), which are initially used as previously reported and then dynamically adapted during the use of the system. Our results suggest that ARR provide a safer and more reliable stimulation pattern than fixed restriction rules.