OBJECTIVE: The ease of use and number of degrees of freedom of current myoelectric hand prostheses is limited by the information content and reliability of the surface electromyography (sEMG) signals used to control them. For example, cross-talk limits the capacity to pick up signals from small or deep muscles, such as the forearm muscles for distal arm amputations, or sites of targeted muscle reinnervation (TMR) for proximal amputations. Here we test if signals recorded from the fully implanted, induction-powered wireless Myoplant system allow long-term decoding of continuous as well as discrete movement parameters with better reliability than equivalent sEMG recordings. The Myoplant system uses a centralized implant to transmit broadband EMG activity from four distributed bipolar epimysial electrodes. APPROACH: Two Rhesus macaques received implants in their backs, while electrodes were placed in their upper arm. One of the monkeys was trained to do a cursor task via a haptic robot, allowing us to control the forces exerted by the animal during arm movements. The second animal was trained to perform a center-out reaching task on a touchscreen. We compared the implanted system with concurrent sEMG recordings by evaluating our ability to decode time-varying force in one animal and discrete reach directions in the other from multiple features extracted from the raw EMG signals. MAIN RESULTS: In both cases, data from the implant allowed a decoder trained with data from a single day to maintain an accurate decoding performance during the following months, which was not the case for concurrent surface EMG recordings conducted simultaneously over the same muscles. SIGNIFICANCE: These results show that a fully implantable, centralized wireless EMG system is particularly suited for long-term stable decoding of dynamic movements in demanding applications such as advanced forelimb prosthetics in a wide range of configurations (distal amputations, TMR).
OBJECTIVE: The ease of use and number of degrees of freedom of current myoelectric hand prostheses is limited by the information content and reliability of the surface electromyography (sEMG) signals used to control them. For example, cross-talk limits the capacity to pick up signals from small or deep muscles, such as the forearm muscles for distal arm amputations, or sites of targeted muscle reinnervation (TMR) for proximal amputations. Here we test if signals recorded from the fully implanted, induction-powered wireless Myoplant system allow long-term decoding of continuous as well as discrete movement parameters with better reliability than equivalent sEMG recordings. The Myoplant system uses a centralized implant to transmit broadband EMG activity from four distributed bipolar epimysial electrodes. APPROACH: Two Rhesus macaques received implants in their backs, while electrodes were placed in their upper arm. One of the monkeys was trained to do a cursor task via a haptic robot, allowing us to control the forces exerted by the animal during arm movements. The second animal was trained to perform a center-out reaching task on a touchscreen. We compared the implanted system with concurrent sEMG recordings by evaluating our ability to decode time-varying force in one animal and discrete reach directions in the other from multiple features extracted from the raw EMG signals. MAIN RESULTS: In both cases, data from the implant allowed a decoder trained with data from a single day to maintain an accurate decoding performance during the following months, which was not the case for concurrent surface EMG recordings conducted simultaneously over the same muscles. SIGNIFICANCE: These results show that a fully implantable, centralized wireless EMG system is particularly suited for long-term stable decoding of dynamic movements in demanding applications such as advanced forelimb prosthetics in a wide range of configurations (distal amputations, TMR).
Authors: Laura Becerra-Fajardo; Marc Oliver Krob; Jesus Minguillon; Camila Rodrigues; Christine Welsch; Marc Tudela-Pi; Albert Comerma; Filipe Oliveira Barroso; Andreas Schneider; Antoni Ivorra Journal: J Neuroeng Rehabil Date: 2022-06-07 Impact factor: 5.208
Authors: Henry T Lancashire; Yazan Al Ajam; Robert P Dowling; Catherine J Pendegrass; Gordon W Blunn Journal: Plast Reconstr Surg Glob Open Date: 2019-09-23
Authors: Martin Aman; Christopher Festin; Matthias E Sporer; Clemens Gstoettner; Cosima Prahm; Konstantin D Bergmeister; Oskar C Aszmann Journal: Wien Klin Wochenschr Date: 2019-06-14 Impact factor: 1.704
Authors: Anton M Unakafov; Sebastian Möller; Igor Kagan; Alexander Gail; Stefan Treue; Fred Wolf Journal: PLoS One Date: 2018-08-31 Impact factor: 3.240