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Peer-reviewedCC BYCell Reports Physical Science

User-in-the-loop adaptive mechanostimulation using structurally engineered hybrid piezoresistive force sensing

  • Zixin Huang1
  • Junjie Lu2
  • Chengsong Yu2
  • Mengli Sui3
  • Jie Cao4
  • Hao Liu2
  • Peng Huang2
  • Long Cheng5
  • Jin Ma6
  • Shengyuan Huang2
  • Wen Gong7
  • Ming Wang8
1 more authors
  • Yu Wang9
9 affiliations
  1. 1School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China; Corresponding author
  2. 2School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China
  3. 3Institute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei 230026, China; Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong 999077, China; Corresponding author
  4. 4College of Integrated Circuits and Micro-Nano Electronics, Fudan University, Shanghai 200433, China
  5. 5School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China
  6. 6School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China; Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
  7. 7Research Center for Advanced Functional Ceramics, Wuzhen Laboratory, Jiaxing, 314599, China
  8. 8College of Integrated Circuits and Micro-Nano Electronics, Fudan University, Shanghai 200433, China; Corresponding author
  9. 9School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China; Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Corresponding author
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Abstract

Repetitive stooping in labor-intensive work induces back muscle fatigue, requiring precise adaptive mechanostimulation. Existing systems suffer from inaccurate force and preload detection, resulting in unstable contact and inconsistent pressure delivery. Here, we present a sensing-driven adaptive mechanostimulation system based on a hybrid piezoresistive force sensor. The sensor integrates a pyramid-microstructured element for high-sensitivity preload detection and a bulk composite element enabling wide force range sensing. The system incorporates a lightweight cable-driven positioning module, a soft therapy actuator, and a wearable robotic framework. This architecture enables precise stimulation of fatigue-related muscles identified via multichannel surface electromyography, while the hybrid sensor provides preload calibration and real-time force regulation, improving contact stability and therapeutic precision. In a controlled study with 15 participants, the system demonstrates significantly better recovery outcomes than thermal stimulation ( p = 0.0025) and preprogrammed mechanostimulation ( p = 0.0003). This work establishes a sensing-driven adaptive mechanostimulation paradigm for interactive fatigue rehabilitation.

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Cite

BibTeXRIS
BibTeX
@article{huang2026userintheloop,
  title = {{User-in-the-loop adaptive mechanostimulation using structurally engineered hybrid piezoresistive force sensing}},
  author = {Zixin Huang and Junjie Lu and Chengsong Yu and Mengli Sui and Jie Cao and Hao Liu and Peng Huang and Long Cheng and Jin Ma and Shengyuan Huang and Wen Gong and Ming Wang and Yu Wang},
  journal = {Cell Reports Physical Science},
  year = {2026},
  month = oct,
  doi = {10.1016/j.xcrp.2026.103505},
  url = {https://doi.org/10.1016/j.xcrp.2026.103505},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - User-in-the-loop adaptive mechanostimulation using structurally engineered hybrid piezoresistive force sensing
AU  - Zixin Huang
AU  - Junjie Lu
AU  - Chengsong Yu
AU  - Mengli Sui
AU  - Jie Cao
AU  - Hao Liu
AU  - Peng Huang
AU  - Long Cheng
AU  - Jin Ma
AU  - Shengyuan Huang
AU  - Wen Gong
AU  - Ming Wang
AU  - Yu Wang
PY  - 2026
DA  - 2026/10/23
JO  - Cell Reports Physical Science
DO  - 10.1016/j.xcrp.2026.103505
UR  - https://doi.org/10.1016/j.xcrp.2026.103505
AB  - Repetitive stooping in labor-intensive work induces back muscle fatigue, requiring precise adaptive mechanostimulation. Existing systems suffer from inaccurate force and preload detection, resulting in unstable contact and inconsistent pressure delivery. Here, we present a sensing-driven adaptive mechanostimulation system based on a hybrid piezoresistive force sensor. The sensor integrates a pyramid-microstructured element for high-sensitivity preload detection and a bulk composite element enabling wide force range sensing. The system incorporates a lightweight cable-driven positioning module, a soft therapy actuator, and a wearable robotic framework. This architecture enables precise stimulation of fatigue-related muscles identified via multichannel surface electromyography, while the hybrid sensor provides preload calibration and real-time force regulation, improving contact stability and therapeutic precision. In a controlled study with 15 participants, the system demonstrates significantly better recovery outcomes than thermal stimulation ( p = 0.0025) and preprogrammed mechanostimulation ( p = 0.0003). This work establishes a sensing-driven adaptive mechanostimulation paradigm for interactive fatigue rehabilitation.
SN  - 2666-3864
ER  -