Dataset Information

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

ABSTRACT: 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.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.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.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.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.

SUBMITTER: Pasquina PF

PROVIDER: S-EPMC4317373 | biostudies-literature | 2015 Apr

REPOSITORIES: biostudies-literature

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

Pasquina Paul F PF Evangelista Melissa M Carvalho A J AJ Lockhart Joseph J Griffin Sarah S Nanos George G McKay Patricia P Hansen Morten M Ipsen Derek D Vandersea James J Butkus Josef J Miller Matthew M Murphy Ian I Hankin David D

Journal of neuroscience methods 20140804

<h4>Background</h4>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, who ...[more]

PMID: 25102286

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Project description:The overarching goal was to resolve a major barrier to real-life prosthesis usability-the rapid degradation of prosthesis control systems, which require frequent recalibrations. Specifically, we sought to develop and test a motor decoder that provides (1) highly accurate, real-time movement response, and (2) unprecedented adaptability to dynamic changes in the amputee's biological state, thereby supporting long-term integrity of control performance with few recalibrations. To achieve that, an adaptive motor decoder was designed to auto-switch between algorithms in real-time. The decoder detects the initial aggregate motoneuron spiking activity from the motor pool, then engages the optimal parameter settings for decoding the motoneuron spiking activity in that particular state. "Clear-box" testing of decoder performance under varied physiological conditions and post-amputation complications was conducted by comparing the movement output of a simulated prosthetic hand as driven by the decoded signal vs. as driven by the actual signal. Pearson's correlation coefficient and Normalized Root Mean Square Error were used to quantify the accuracy of the decoder's output. Our results show that the decoder algorithm extracted the features of the intended movement and drove the simulated prosthetic hand accurately with real-time performance (<10 ms) (Pearson's correlation coefficient >0.98 to >0.99 and Normalized Root Mean Square Error <13-5%). Further, the decoder robustly decoded the spiking activity of multi-speed inputs, inputs generated from reversed motoneuron recruitment, and inputs reflecting substantial biological heterogeneity of motoneuron properties, also in real-time. As the amputee's neuromodulatory state changes throughout the day and the electrical properties and ratio of slower vs. faster motoneurons shift over time post-amputation, the motor decoder presented here adapts to such changes in real-time and is thus expected to greatly enhance and extend the usability of prostheses.

Dataset Information

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

Publications

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

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