Dupan S

Dyson M

Krasoulis A

Nazarpour K

Newcastle University E-Prints

   Abstract— In this paper we present results from five real-time myoelectric control experiments with able-bodied and amputee participants comprising three distinct paradigms, namely, two-dimensional cursor position control on a screen; classification-based grasp control of a prosthetic hand; and prosthetic control of individual fingers. We observe a consistent trend across this spectrum of myoelectric interfaces, that is, regardless of the level of intuitiveness of the interface, users can improve their performance in biofeedback myoelectric control tasks with training. We argue that progress in prosthetic limb control can be best achieved through a strong synergy of signal processing algorithms, motor learning, and feedback.  I. INTRODUCTION Acquiring a new skill requires accurate motor commands to be sent from the brain to the hand, and reliable sensory and visual feedback to the brain. Over time and with training, the brain learns to handle this two-way communication flexibly and efficiently. Inspired by this sensorimotor interplay, our research is primarily focused on the study of motor learning during myoelectric control tasks. We argue that real progress in the field of prosthetic control can only be achieved by explicitly taking into account the effect of motor learning and user adaptation on myoelectric control performance. II. EXPERIMENTS Here, we report results from five distinct experiments involving able-bodied and trans-radial amputee subjects. All experiments were approved by local Ethical Committees of Newcastle University, University of Edinburgh, and Imperial College and informed consent was acquired in written form from all participants prior to the experimental sessions.  Our experiments comprised two types of decoders, namely, abstract decoders, whereby a pre-determined, non-intuitive mapping existed between muscle activity and target degrees-of-freedom (DOFs), hence the users had to learn an inverse model of the interface; and intuitive decoders, whereby motor intent was deciphered using machine learning models trained with subject-specific data. In Experiment 1, participants were instructed to control the cursor position on a screen using three muscle pairs, including both extrinsic and  *Research supported by Engineering and Physical Sciences Research Council (EPSRC), UK. A. Krasoulis was with University of Edinburgh, UK. He is currently with Newcastle University, UK (corresponding author; phone: +44 (0) 191 20 86682).  M. Dyson is with Newcastle University, UK. S. S. G. Dupan was with Radboud University, the Netherlands. She is currently with Newcastle University, UK. K. Nazarpour is with Newcastle University, UK.  E-mail: {Agamemnon.Krasoulis, Matthew.Dyson, Sigrid.Dupan, Kianoush.Nazarpour}@newcastle.ac.uk intrinsic hand muscles, by using an abstract myoelectric control scheme [1]. Similarly, in Experiment 2, an abstract decoder was used to control the position of four degrees-of-freedom (DOFs) of a robotic hand using measurements from four intrinsic hand muscles [2]. In experiment 3, an intuitive decoder (i.e., a classifier) was used to decode grasp movement using electromyography (EMG) recordings from the proximal part of the forearm. Predictions were used to control a commercial prosthetic hand in real-time during a grasp-carry-release task [3]. In experiment 4, a regression-based intuitive decoder was used to control the position of five DOFs of a robotic hand, including thumb abduction/adduction and flexion/extension of all fingers using forearm EMG. A control experimental condition in which participants tele-operated the robotic hand using an instrumented data glove was also included [3-4]. Finally, in Experiment 5, a regression-based intuitive decoder was used to estimate fingertip force of four fingers using high-density EMG measurements form the forearm. Information on the estimated forces was fed back to the users on a screen [5]. III. RESULTS In all experiments, an increase in performance with training was observed, regardless of the type of recorded muscles (i.e., intrinsic vs. extrinsic) or decoder. This trend was consistent across able-bodied and amputee subjects. As was to be expected, the effect of training on task performance was more profound in abstract [1-2] than in intuitive decoders [3-5]. Nevertheless, the performance improvement in the latter case was still significant, even in the case of robotic hand tele-operation using a data glove. Dimensionality reduction analysis revealed substantial changes in muscle co-activation structure during closed-loop myoelectric control. ACKNOWLEDGMENT The authors thank the amputee volunteers who took part in the experiments.  REFERENCES [1] M. Dyson, J. Barnes, K. Nazarpour, “Myoelectric Control with Abstract Decoders”,  in J. Neur. Eng., vol. 15, Jul. 2018, pp. 056003. [2] T. Pistohl, C. Cipriani, A. Jackson, K. Nazarpour, “Abstract and proportional myoelectric control for multi-fingered hand prostheses”, in Ann. Biomed. Eng., vol. 41, Dec. 2013, pp. 2687—2698. [3] A. Krasoulis, “Machine learning-based dexterous control of hand prostheses”, PhD Thesis, University of Edinburgh, UK, 2018. [4] A. Krasoulis, S. Vijayakumar, K. Nazarpour, “Effect of motor learning on prosthetic finger control with an intuitive myoelectric decoder”, in Scientific Reports, In press. [5] S. Dupan, “From man to machine – Neural control of fine hand movements”, PhD Thesis, Radboud University, the Netherlands, 2018. Motor Learning for Control of Prosthetic Limbs Agamemnon Krasoulis, Matthew Dyson, Sigrid S. G. Dupan, Kianoush Nazarpour, Senior Member, IEEE 

Motor learning for control of prosthetic limbs

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