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This paper contains the way of making a portable acquisition system of a sEMG signal from the extensor digitorum muscle with real-time processing of this signal to generate hand grip force information. The system represents a viable option for controlling actuators of a robotic hand or exoskeletons. The system is built with a Biometrics SX230FW sensor, a Raspberry Pi 4 single-board computer equipped with an MCC 118 data acquisition module and a custom-made hand grip force measurement device based on a load cell and the HX711 analog-to-digital converter. This study show cases the potential to transform muscle monitoring and rehabilitation with an accessible, portable, and efficient system built on advanced technologies. The real-time data utilization and seamless integration with other technologies pave the way for new research opportunities and practical applications across various fields, potentially leading to advancements in wearable devices, prosthetics, and human-robot interaction systems for enhanced mobility and rehabilitation outcomes.

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Portable System for Processing sEMG Signals with Neural Network Models for Measuring Hand Grip Force

  • 会议论文

  • 发表日期：

    2025年

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  • Corina-Ioana Cobzac,
  • Mihai Avram

摘要

This paper contains the way of making a portable acquisition system of a sEMG signal from the extensor digitorum muscle with real-time processing of this signal to generate hand grip force information. The system represents a viable option for controlling actuators of a robotic hand or exoskeletons. The system is built with a Biometrics SX230FW sensor, a Raspberry Pi 4 single-board computer equipped with an MCC 118 data acquisition module and a custom-made hand grip force measurement device based on a load cell and the HX711 analog-to-digital converter. This study show cases the potential to transform muscle monitoring and rehabilitation with an accessible, portable, and efficient system built on advanced technologies. The real-time data utilization and seamless integration with other technologies pave the way for new research opportunities and practical applications across various fields, potentially leading to advancements in wearable devices, prosthetics, and human-robot interaction systems for enhanced mobility and rehabilitation outcomes.