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Analysis of myoelectric signals using a Field Programmable SoC

Authors
Publisher
IEEE
Publication Date
Keywords
  • Qa75 Electronic Computers. Computer Science / Számítástechnika
  • Számítógéptudomány
Disciplines
  • Computer Science
  • Design

Abstract

Analysis of myoelectric signals using a Field Programmable SoC Bence J. Borbe´ly∗, Zolta´n Kincses†, Zsolt Vo¨ro¨ha´zi‡, Zolta´n Nagy∗§, Pe´ter Szolgay∗§ ∗Faculty of Information Technology, Pa´zma´ny Pe´ter Catholic University, Budapest, H-1083 †Institute of Informatics, University of Szeged, Szeged, H-6701 ‡Dept. of Electrical Engineering and Information Systems, University of Pannonia, Veszpre´m, H-8200 §Cellular Sensory and Optical Wave Computing Laboratory, Hungarian Academy of Sciences, Budapest, H-1111 Abstract—A platform design for the analysis of human myoelectric signals (MES) is presented. Offline recorded multi- channel signals of forearm muscles are processed with a Field Programmable SoC in order to classify different movement pat- terns to control human-assisting electromechanical systems with multiple degrees of freedom (e.g. a prosthetic hand). Benchmark results of an ANSI C implementation are shown to assess the raw performance of the built-in ARM cores of the SoC. Possible computational bottlenecks are located based on the results and custom hardware implementations are shown to fully utilize the flexibility and performance of the used hardware platform. I. INTRODUCTION The non-invasive measurement and analysis of human bioelectric signals has been an emerging field in the last decades. Electric signals measured at different skin surface locations have different characteristics. They can carry impor- tant features of an individual’s current state of health via heart monitoring (electrocardiogram, ECG), they can drive Brain- Computer Interfaces if measured from the surface of the head (electroencephalogram, EEG) or can tell specific movement intents of patients with limb amputations if measured from the covering skin of residual muscles (myoelectric signal, MES), especially in the case of upper limb amputations. In this study we focus on the processing and classification of MES data to utilize the flexibility and performance of a Field Programmable platform in a

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