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.

It has been estimated that more than 1.6 million individuals in the United States have undergone at least one amputation. The literature abounds with research of the classifications of such injuries, their etiologies, epidemiologies, treatment regimens, average age of onset (average age of amputation), and much more. The subpopulation that is often overlooked in these evaluations, however, is comprised of individuals who have suffered multiple limb loss. The challenges faced by those with single-limb loss are amplified for those with multiple limb loss. Pain, lifestyle adjustment, and quality of life return are just a few key areas of concern in this population. Along with amputations resulting from trauma, many individuals with multiple amputations have endured them as a result of dysvascular disease. Over recent years, amputations as a result of dysvascular disease have risen to comprise more than 80 % of new amputations occurring in the United States every year. This compares to just 54 % of total current prevalence. Those with diabetes comorbid with dysvascular disease make up 74 % of those with dysvascular amputations, and these individuals with diabetes comorbid with dysvascular disease have a 55 % chance of enduring an amputation of their contralateral limb within 2–3 years of their initial amputation. With the well-documented aging of the nation’s population and the similarly skyrocketing prevalence of dysvascular disease and diabetes, it can be expected that the number of individuals with multiple limb loss will continue to increase in the United States. This article outlines the recommended measures of care for this particular subpopulation, including pain management, behavioral health considerations, strategies for rehabilitation for various levels and variations of multiple limb loss, and the assistive technology and adaptive equipment that might be available for these individuals to best enable them to continue healthy, fulfilling lives following amputation.

A longstanding engineering ambition has been to design anthropomorphic bionic limbs: devices that look like and are controlled in the same way as the biological body (biomimetic). The untested assumption is that biomimetic motor control enhances device embodiment, learning, generalization, and automaticity. To test this, we compared biomimetic and non-biomimetic control strategies for able-bodied participants when learning to operate a wearable myoelectric bionic hand. We compared motor learning across days and behavioural tasks for two training groups: Biomimetic (mimicking the desired bionic hand gesture with biological hand) and Arbitrary control (mapping an unrelated biological hand gesture with the desired bionic gesture). For both trained groups, training improved bionic limb control, reduced cognitive reliance, and increased embodiment over the bionic hand. Biomimetic users had more intuitive and faster control early in training. Arbitrary users matched biomimetic performance later in training. Further, arbitrary users showed increased generalization to a novel control strategy. Collectively, our findings suggest that biomimetic and arbitrary control strategies provide different benefits. The optimal strategy is likely not strictly biomimetic, but rather a flexible strategy within the biomimetic to arbitrary spectrum, depending on the user, available training opportunities and user requirements.