A Modular, Smart, and Wearable System for High Density sEMG Detection

IEEE Trans Biomed Eng. 2019 Dec;66(12):3371-3380. doi: 10.1109/TBME.2019.2904398. Epub 2019 Mar 11.

Abstract

Objective: The use of linear or bi-dimensional electrode arrays for surface EMG detection (HD-sEMG) is gaining attention as it increases the amount and reliability of information extracted from the surface EMG. However, the complexity of the setup and the encumbrance of HD-sEMG hardware currently limits its use in dynamic conditions. The aim of this paper was to develop a miniaturized, wireless, and modular HD-sEMG acquisition system for applications requiring high portability and robustness to movement artifacts.

Methods: A system with modular architecture was designed. Its core is a miniaturized 32-channel amplifier (Sensor Unit - SU) sampling at 2048 sps/ch with 16 bit resolution and wirelessly transmitting data to a PC or a mobile device. Each SU is a node of a Body Sensor Network for the synchronous signal acquisition from different muscles.

Results: A prototype with two SUs was developed and tested. Each SU is small (3.4 cm × 3 cm × 1.5 cm), light (16.7 g), and can be connected directly to the electrodes; thus, avoiding the need for customary, wired setup. It allows to detect HD-sEMG signals with an average noise of 1.8 μVRMS and high performance in terms of rejection of power-line interference and motion artefacts. Tests performed on two SUs showed no data loss in a 22 m range and a ±500 μs maximum synchronization delay.

Conclusions: Data collected in a wide spectrum of experimental conditions confirmed the functionality of the designed architecture and the quality of the acquired signals.

Significance: By simplifying the experimental setup, reducing the hardware encumbrance, and improving signal quality during dynamic contractions, the developed system opens new perspectives in the use of HD-sEMG in applied and clinical settings.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Artifacts
  • Electromyography / instrumentation*
  • Electromyography / methods*
  • Equipment Design
  • Humans
  • Movement / physiology
  • Signal Processing, Computer-Assisted / instrumentation*
  • Wearable Electronic Devices*