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Related Experiment Video

Updated: Oct 8, 2025

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects
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A Coupled Piezoelectric Sensor for MMG-Based Human-Machine Interfaces.

Mateusz Szumilas1, Michał Władziński1, Krzysztof Wildner1

  • 1Warsaw University of Technology, Faculty of Mechatronics, Institute of Metrology and Biomedical Engineering, A. Boboli 8 St., 02-525 Warsaw, Poland.

Sensors (Basel, Switzerland)
|December 28, 2021
PubMed
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This study introduces a novel mechanomyography (MMG) sensor for improved human-machine interfaces (HMI). The new sensor design enables accurate muscle activity recording and gesture classification with simplified spatial arrangements.

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Engineering
  • Human-Machine Interfaces

Background:

  • Mechanomyography (MMG) is a promising technique for muscle activity recording in human-machine interfaces (HMI).
  • Sensor design and spatial distribution are critical for effective MMG-based HMI implementation.
  • Existing MMG sensors may present limitations in terms of complexity and spatial requirements.

Purpose of the Study:

  • To present a novel mechanomyography (MMG) sensor design for enhanced human-machine interface (HMI) applications.
  • To evaluate the functionality of the new MMG sensor in static force estimation and dynamic gesture classification.
  • To demonstrate the potential for simplified spatial arrangements in MMG-based HMI systems.

Main Methods:

  • Development of a new MMG sensor comprising two coupled piezoelectric discs in a single housing.
Keywords:
convolutional neural networkhand gesture recognitionhuman-machine interfacemechanomyographypiezoelectric sensorprosthetic controlvibration sensor

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  • Experimental validation involving static force/MMG relationship estimation up to 60% of maximal voluntary contraction.
  • Implementation of a neural network for classifying eight hand gestures based on single-site forearm MMG data.

    • Main Results:

    • An exponential relationship was observed between MMG signals and exerted force under static conditions.
    • High classification accuracy of 94.3% was achieved for eight distinct hand motions using the developed MMG sensor.
    • The novel sensor design facilitates a simplified spatial arrangement for MMG-based HMI.

    Conclusions:

    • The presented MMG sensor design is effective for both static force assessment and dynamic gesture recognition.
    • This innovation simplifies the spatial configuration of MMG-based HMI systems.
    • The developed sensor holds significant potential for advancing the field of muscle-controlled human-machine interfaces.