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Purpose <p>Traditional hypertonia diagnosis relies on the Modified Ashworth Scale (MAS), which is subjective and dependent on doctors’ experience. Although previous studies have explored the use of force sensors and surface electromyography (sEMG), finding a reliable and valid detection method remains a challenge. This study aims to develop a simple yet effective platform that integrates biomechanical and sEMG data for upper-limb muscle tone assessment, providing a more objective and quantitative evaluation approach.</p> Methods <p>A detection platform was developed to collect biomechanical and sEMG data from 59 subjects, including 49 patients (MAS Ⅰ = 21, MAS Ⅰ +  = 16, MAS Ⅱ = 12) and 10 healthy individuals, at different movement speeds (15°/s, 20°/s, and 25°/s). The acquired data underwent feature extraction, including signal processing and statistical analysis. Dimensionality reduction was applied to optimize the extracted features, and these features were then integrated into a classification algorithm for further analysis.</p> Results <p>The extracted features effectively distinguished patients from healthy individuals, with statistically significant differences (<i>p</i> &lt; 0.01). Furthermore, the strong correlation between the extracted features and MAS scores (<i>p</i> &lt; 0.01) confirmed the reliability of the proposed method. Finally, the classification algorithm demonstrated high consistency with clinical evaluations, validating its potential for clinical application in muscle tone assessment.</p> Conclusion <p>This study introduces an objective and quantitative method for assessing muscle tone, shifting away from the traditional subjective MAS evaluation. By enhancing diagnostic accuracy, the proposed approach provides a more reliable basis for hypertonia diagnosis and treatment. The findings hold significant promise for optimizing clinical decision-making, ultimately improving patient management and therapeutic strategies.</p>

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Portable Upper-limb Muscle Tone Assessment by Integrating Multi-sensor Signals

  • 期刊论文

  • 发表日期：

    2026年2月23日

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  • Yue Zhang,
  • Ying Zheng,
  • Hao ShangGuan,
  • Ting Chen,
  • Wan-Zhu Wang,
  • Ya-Lan Wang,
  • Peiqiang Lin,
  • Bingwei He,
  • Wenyao Hong,
  • Xin-Yuan Chen

摘要

Purpose

Traditional hypertonia diagnosis relies on the Modified Ashworth Scale (MAS), which is subjective and dependent on doctors’ experience. Although previous studies have explored the use of force sensors and surface electromyography (sEMG), finding a reliable and valid detection method remains a challenge. This study aims to develop a simple yet effective platform that integrates biomechanical and sEMG data for upper-limb muscle tone assessment, providing a more objective and quantitative evaluation approach.

Methods

A detection platform was developed to collect biomechanical and sEMG data from 59 subjects, including 49 patients (MAS Ⅰ = 21, MAS Ⅰ +  = 16, MAS Ⅱ = 12) and 10 healthy individuals, at different movement speeds (15°/s, 20°/s, and 25°/s). The acquired data underwent feature extraction, including signal processing and statistical analysis. Dimensionality reduction was applied to optimize the extracted features, and these features were then integrated into a classification algorithm for further analysis.

Results

The extracted features effectively distinguished patients from healthy individuals, with statistically significant differences (p < 0.01). Furthermore, the strong correlation between the extracted features and MAS scores (p < 0.01) confirmed the reliability of the proposed method. Finally, the classification algorithm demonstrated high consistency with clinical evaluations, validating its potential for clinical application in muscle tone assessment.

Conclusion

This study introduces an objective and quantitative method for assessing muscle tone, shifting away from the traditional subjective MAS evaluation. By enhancing diagnostic accuracy, the proposed approach provides a more reliable basis for hypertonia diagnosis and treatment. The findings hold significant promise for optimizing clinical decision-making, ultimately improving patient management and therapeutic strategies.