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Related Experiment Video

Updated: Jul 16, 2026

Setup for the Quantitative Assessment of Motion and Muscle Activity During a Virtual Modified Box and Block Test
04:06

Setup for the Quantitative Assessment of Motion and Muscle Activity During a Virtual Modified Box and Block Test

Published on: January 12, 2024

Depth-Sensitive Optical Sensing for Non-Invasive Measurement of Human Muscle Activity.

Kazunari Matsuo1, D S V Bandara1, Hirofumi Nogami2

  • 1Faculty of Engineering, Kyushu University, 744 Moto-o ka, Nishi-ku, Fukuoka 819-0395, Japan.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

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This study introduces a novel optical sensor for depth-sensitive muscle activity measurement. The wearable device accurately distinguishes hand and wrist movements, showing potential for advanced human-machine interfaces.

Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Human-Machine Interfaces

Background:

  • Conventional surface electromyography (sEMG) and mechanomyography (MMG) offer limited depth-dependent muscle information.
  • Distinguishing complex movements requires sensing activity from both superficial and deeper muscle layers.
  • Current non-invasive techniques struggle to provide granular, depth-resolved physiological data.

Purpose of the Study:

  • To develop and evaluate a compact, wearable optical sensor for depth-sensitive physiological measurements.
  • To assess the sensor's capability in discriminating various hand and wrist movements.
  • To explore the utility of multi-distance source-detector measurements for enhanced motion classification.

Main Methods:

  • Designed a wearable optical sensor with a light source and photodetectors at six source-detector distances (12-48 mm).
Keywords:
depth-sensitive muscle activityhuman–machine interfacenear-infraredoptical sensingwearable sensors

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  • Validated depth sensitivity using Monte Carlo simulations and phantom experiments.
  • Collected forearm muscle signals during nine hand/wrist movements and applied machine learning (LDA) for classification.

    • Main Results:

    • The optical sensor demonstrated distinct depth sensitivity profiles across different source-detector distances.
    • Linear Discriminant Analysis achieved 87.5% average classification accuracy using all six sensor channels.
    • Ablation studies confirmed that incorporating multiple sensing depths systematically improved motion discrimination performance.

    Conclusions:

    • Multi-distance optical sensing is feasible for capturing depth-dependent physiological information non-invasively.
    • The developed sensor provides complementary data from various muscle depths, enhancing motion pattern discrimination.
    • This technology shows significant potential for compact, wearable human-machine interface applications.