Long-term decoding of movement force and direction with a wireless myoelectric implant

Pierre Morel,Lait Abu-Saleh,Enrico Ferrea,Wolfgang Krautschneider,Klaus-Peter Hoffmann,Roman Ruff,Hans Dietl,Josep Marcel Cardona Audí,Bahareh Taghizadeh-Sarshouri,Alexander Gail,Michael Russold,Sören Lewis,Dietmar Schroeder,Thomas Meiners

doi:10.1088/1741-2560/13/1/016002

Abstract

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).

Highlights

Human–machine interfaces have been extensively studied in the last decade, in the context of motor function restoration in impaired patients
We test if signals recorded from the fully implanted, induction-powered wireless Myoplant system allow longterm decoding of continuous as well as discrete movement parameters with better reliability than equivalent surface electromyography (sEMG) recordings
We present the result of long-term implantation of this system in two monkeys, and demonstrate the validity of this wireless intramuscular EMG (iEMG) approach for long-term prosthetic by comparing it to equivalent sEMG recordings

Summary

Introduction

Human–machine interfaces have been extensively studied in the last decade, in the context of motor function restoration in impaired patients. When still obtainable, the electrical signals provided by the peripheral nervous system have proven to be ideal to control prostheses with high-degrees of freedom [4]. A 2-DOF stateof-the-art prosthesis (Michelangelo Hand, Otto Bock Healthcare GmbH) allows closing/opening of the hand with different grip types, and uses surface EMG (sEMG) activity from a pair of muscles for control. The control is robust, but is necessarily sequential: users open and close the hand by contracting one or the other muscle, and switch the grip type by cocontracting these muscles. While such prostheses significantly improve the patients’ quality of life, users would benefit greatly from faster and more intuitive control of the hand

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Discussion

Conclusion