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

• Published in: ACS sensors Vol. 10; no. 8; pp. 5484 - 5494
• Main Authors: Zhang, Hongyu, Wang, Keer, Suo, Jiao, Cheng, Clio Yuen Man, Chen, Meng, Lai, King Wai Chiu, Or, Calvin Kalun, Hu, Yong, Roy, Vellaisamy A. L., Lam, Cindy Lo Kuen, Xi, Ning, Lou, Vivian W. Q., Li, Wen Jung
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 22.08.2025
• Subjects: Adult; Aged; Dimethylpolysiloxanes - chemistry; Female; Gait - physiology; Humans; Male; Middle Aged; Monitoring, Physiologic - instrumentation; Muscle, Skeletal - physiology; Nanocomposites - chemistry; Nanotubes, Carbon - chemistry; Porosity; Wearable Electronic Devices; Young Adult; muscle mass; CNT/PDMS flexible muscle sensor; machine learning; muscle function; YOLO-V5m
• ISSN: 2379-3694; 2379-3694
• DOI: 10.1021/acssensors.4c03379

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–1 (0–7 kPa) and 0.000922 kPa–1 (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.