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Low-Cost, Wireless Bioelectric Signal Acquisition and Classification Platform
  • +3
  • Eric J Earley,
  • Nathaly Sánchez Chan,
  • Autumn Naber,
  • Enzo Mastinu,
  • Minh T N Truong,
  • Max Ortiz-Catalan
Eric J Earley
Department of Orthopedics, University of Colorado School of Medicine, Bone Anchored Limb Research Group, University of Colorado, Department of Electrical Engineering, Chalmers University of Technology, Center for Bionics & Pain Research
Author Profile
Nathaly Sánchez Chan
Department of Electrical Engineering, Chalmers University of Technology, Center for Bionics & Pain Research
Autumn Naber
Department of Electrical Engineering, Chalmers University of Technology
Enzo Mastinu
BioRobotics Institute, Scuola Superiore Sant'Anna, Department of Electrical Engineering, Chalmers University of Technology, Center for Bionics & Pain Research
Minh T N Truong
MoveAbility Lab, KTH Royal Institute of Technology, Center for Bionics & Pain Research
Max Ortiz-Catalan
University of Melbourne, Bionics Institute, Department of Electrical Engineering, Chalmers University of Technology, Center for Bionics & Pain Research

Corresponding Author:

Abstract

Bioelectric signal classification is a flourishing area of biomedical research, however conducting this research in a clinical setting can be difficult to achieve. The lack of inexpensive acquisition hardware can limit researchers from collecting and working with real-time data. Furthermore, hardware requiring direct connection to a computer can impose restrictions on typically mobile clinical settings for data collection. Here, we present an open-source ADS1299-based bioelectric signal acquisition system with wireless capability suitable for mobile data collection in clinical settings. This system is based on the ADS_BP and BioPatRec, both open-source bioelectric signal acquisition hardware and MATLAB-based pattern recognition software, respectively. We provide 3D-printable housing enabling the hardware to be worn by users during experiments and demonstrate the suitability of this platform for real-time signal acquisition and classification. In conjunction, these developments provide a unified hardware-software platform for a cost of around $150 USD. This device can enable researchers and clinicians to record bioelectric signals from able-bodied or motor-impaired individuals in laboratory or clinical settings, and to perform offline or real-time intent classification for the control of robotic and virtual devices.
10 Jan 2024Submitted to TechRxiv
18 Jan 2024Published in TechRxiv