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

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<sup>-1</sup> (0-7 kPa) and 0.000922 kPa<sup>-1</sup> (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.

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Publication Details

Journal
ACS Sensors
Published
2025-07-24
DOI
https://doi.org/10.1021/acssensors.4c03379
Citations
3
Primary Topic
Advanced Sensor and Energy Harvesting Materials
Type
article
Field-Weighted Citation Impact
2.02

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article

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

Yong Hu, King Wai Chiu Lai, Cindy Lo Kuen Lam, Jiao Suo et al.
3 citations
ACS Sensors
Advanced Sensor and Energy Harvesting Materials
2.02
article

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

Yong Hu, King Wai Chiu Lai, Cindy Lo Kuen Lam, Jiao Suo, Ning Xi, VW Lou, Clio Yuen Man Cheng, Calvin Kalun Or, Wen J. Li, Vellaisamy A. L. Roy, Keer Wang, Meng Chen, Hongyu Zhang
article en
3 citations

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<sup>-1</sup> (0-7 kPa) and 0.000922 kPa<sup>-1</sup> (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.

ACS SensorsVol. 10(8)
City University of Hong Kong (HK), Chinese University of Hong Kong (HK), Li Ka Shing Foundation (CN), University of Houston System (US), Department of Health and Aged Care (AU), Robotics Research (United States) (US), Centre for Automation and Robotics (ES), Primary Health Care (QA), Metropolitan University (BD), University of Hong Kong (HK)
Research Grants Council, University Grants Committee
Openalex Percentile: Top 13%
Advanced Sensor and Energy Harvesting Materials
2.02
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