{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China; Corresponding author",
    "School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China",
    "Institute 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",
    "College of Integrated Circuits and Micro-Nano Electronics, Fudan University, Shanghai 200433, China",
    "School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China",
    "School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China; Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China",
    "Research Center for Advanced Functional Ceramics, Wuzhen Laboratory, Jiaxing, 314599, China",
    "College of Integrated Circuits and Micro-Nano Electronics, Fudan University, Shanghai 200433, China; Corresponding author",
    "School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China; Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; Corresponding author"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 13,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Zixin Huang"
    },
    {
      "affs": [
        1
      ],
      "name": "Junjie Lu"
    },
    {
      "affs": [
        1
      ],
      "name": "Chengsong Yu"
    },
    {
      "affs": [
        2
      ],
      "name": "Mengli Sui"
    },
    {
      "affs": [
        3
      ],
      "name": "Jie Cao"
    },
    {
      "affs": [
        1
      ],
      "name": "Hao Liu"
    },
    {
      "affs": [
        1
      ],
      "name": "Peng Huang"
    },
    {
      "affs": [
        4
      ],
      "name": "Long Cheng"
    },
    {
      "affs": [
        5
      ],
      "name": "Jin Ma"
    },
    {
      "affs": [
        1
      ],
      "name": "Shengyuan Huang"
    },
    {
      "affs": [
        6
      ],
      "name": "Wen Gong"
    },
    {
      "affs": [
        7
      ],
      "name": "Ming Wang"
    },
    {
      "affs": [
        8
      ],
      "name": "Yu Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ax74r10p2hnyj.bib",
  "cited_by": 0,
  "doi": "10.1016/j.xcrp.2026.103505",
  "fulltext_url": "http://www.sciencedirect.com/science/article/pii/S266638642600411X",
  "id": "ax74r10p2hnyj",
  "issns": [
    "2666-3864"
  ],
  "landing_url": "https://doi.org/10.1016/j.xcrp.2026.103505",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "QC1-999"
    ],
    [
      "openalex",
      "2204|Prosthetics and Rehabilitation Robotics"
    ]
  ],
  "openalex": "W7214231309",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "Elsevier",
  "ris": "https://research.draconitic.com/paper/ax74r10p2hnyj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:43:35Z",
      "native_id": "96751dd5cd0f4302a7af73d0b2794329",
      "source": "doaj",
      "url": "https://doi.org/10.1016/j.xcrp.2026.103505"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "lcc:QC1-999",
      "subfield": "general"
    }
  ],
  "title": "User-in-the-loop adaptive mechanostimulation using structurally engineered hybrid piezoresistive force sensing",
  "title_text": "User-in-the-loop adaptive mechanostimulation using structurally engineered hybrid piezoresistive force sensing",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:43:35Z",
  "url": "https://research.draconitic.com/paper/ax74r10p2hnyj",
  "venue": "Cell Reports Physical Science",
  "versions": []
}