A Piezoresistive Array Armband With Reduced Number of Sensors for Hand Gesture Recognition.

Journal: Frontiers in neurorobotics
Published Date: Jan 17, 2020

Abstract

Human machine interfaces (HMIs) are employed in a broad range of applications, spanning from assistive devices for disability to remote manipulation and gaming controllers. In this study, a new piezoresistive sensors array armband is proposed for hand gesture recognition. The armband encloses only three sensors targeting specific forearm muscles, with the aim to discriminate eight hand movements. Each sensor is made by a force-sensitive resistor (FSR) with a dedicated mechanical coupler and is designed to sense muscle swelling during contraction. The armband is designed to be easily wearable and adjustable for any user and was tested on 10 volunteers. Hand gestures are classified by means of different machine learning algorithms, and classification performances are assessed applying both, the 10-fold and leave-one-out cross-validations. A linear support vector machine provided 96% mean accuracy across all participants. Ultimately, this classifier was implemented on an Arduino platform and allowed successful control for videogames in real-time. The low power consumption together with the high level of accuracy suggests the potential of this device for exergames commonly employed for neuromotor rehabilitation. The reduced number of sensors makes this HMI also suitable for hand-prosthesis control.

Authors

  • Daniele Esposito
    Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, Italy.
  • Emilio Andreozzi
    Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, Italy.
  • Gaetano D Gargiulo
    School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW, Australia.
  • Antonio Fratini
    School of Life and Health Sciences, Aston University, Birmingham, United Kingdom.
  • Giovanni D'Addio
    Department of Neurorehabilitation, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy.
  • Ganesh R Naik
    MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Penrith, NSW, Australia.
  • Paolo Bifulco
    Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, Italy.

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