First-in-man demonstration of a fully implanted myoelectric sensors system to control an advanced electromechanical prosthetic hand

Paul F Pasquina, Melissa Evangelista, A J Carvalho, Joseph Lockhart, Sarah Griffin, George Nanos, Patricia McKay, Morten Hansen, Derek Ipsen, James Vandersea, Josef Butkus, Matthew Miller, Ian Murphy, David Hankin
Journal of Neuroscience Methods 2015 April 15, 244: 85-93

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 electro-magnetic 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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