Porous-Structure Flexible Muscle Sensor for Monitoring Muscle Function and Mass.

Journal: ACS sensors
Published Date: Jul 24, 2025

Abstract

Muscle function and composition are important indicators of age-related health. However, current assessment methods are often complex and expensive, making the early detection of related problems difficult. Therefore, developing a cost-effective and easily accessible daily based detection method is an essential research focus. This study introduces a novel portable porous-structured (i.e., CNT/PDMS nanocomposite) and flexible piezoresistive sensor for evaluating muscle function and relative skeletal muscle mass index, offering advantages of cost-effectiveness, safety, and user-friendliness. The porous architecture significantly enhances sensitivity, while the flexible design ensures excellent conformability to the skin and adaptability to complex body movements. The prototype sensor demonstrates a linear detection range of 0-39 kPa with dual-stage sensitivities of 0.03398 kPa (0-7 kPa) and 0.000922 kPa (7-39 kPa). The sensor maintains stable performance for over a week and exhibits reliable operation unaffected by body temperature or perspiration, and the material cost does not exceed 10 HKD. The gait data can be easily collected by wearing the sensor on the left gastrocnemius muscle. Our study encompassed 23 participants from both the elderly and young age groups. The supervised learning achieved a maximum accuracy of 93.48% in distinguishing between the elderly and the young subjects. Unsupervised learning analysis further validated the efficacy of our flexible sensor in muscle function assessment. Additionally, an Adaboost regression model was employed to predict the relative skeletal muscle mass index, achieving a mean error of 2.8%. This flexible sensor demonstrates significant potential for the daily monitoring of muscle function and mass, enabling early detection and prevention of sarcopenia and other muscle-related disorders. Its wearable and noninvasive characteristics make it an attractive solution for muscle assessment in clinical, sports, and home environments.

Authors

  • Hongyu Zhang
    School of Nursing, Wenzhou Medical University, Wenzhou 325035, China.
  • Keer Wang
    Department of Mechanical Engineering, City University of Hong Kong, Hong Kong 999077, China.
  • Jiao Suo
    CAS-CityU Joint Laboratory for Robotic Research, Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong, China.
  • Clio Yuen Man Cheng
    Sau Po Centre on Ageing, The University of Hong Kong, Hong Kong, China (Hong Kong).
  • Meng Chen
    Institute of Industrial and Consumer Product Safety, China Academy of Inspection and Quarantine, Beijing, China.
  • King Wai Chiu Lai
    Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Hong Kong.
  • Calvin Kalun Or
    Department of Industrial and Manufacturing System Engineering, The University of Hong Kong, Hong Kong 999077, China.
  • Yong Hu
    Big Data Decision Institute, Jinan University, Guangzhou, China.
  • Vellaisamy A L Roy
    School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, 999077, P. R. China.
  • Cindy Lo Kuen Lam
    Department of Family Medicine and Primary Care, The University of Hong Kong, Hong Kong, China.
  • Ning Xi
    Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA. Electronic address: xin@egr.msu.edu.
  • Vivian W Q Lou
    Department of Social Work and Social Administration; Sau Po Centre on Ageing, University of Hong Kong, Hong Kong, Hong Kong.
  • Wen Jung Li
    Department of Mechanical Engineering, City University of Hong Kong, Hong Kong, China.

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