ArticleScience advances2025
Flexible multichannel muscle impedance sensors for collaborative human-machine interfaces.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- A skin-conformal liquid metal strain sensor integrating deep learning for neck motion monitoring.iScience · 2026Article
- Materials-Structure-Hardware-Algorithm Co-Driven Hierarchical Optimization for Flexible Sensors.Nano-micro letters · 2026Review
- Skin-mimicking biogel-based iontronic sensor with hierarchical bionic coupling for dexterous tactile e-skin.Nature communications · 2026Article
- Bioinspired Hierarchical Architecture with Water Transport Channels for Strong Adhesion at the Sweating Interface.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Ultraflexible photoelectrical impedance tomography-based imager for 3-axis robotic tactile sensing.Nature communications · 2026Article
- Recent Advances in Decoupling Strategies for Soft Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- [Research progress on flexible wearable sensors for health monitoring].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The demand for advanced human-machine interfaces (HMIs) highlights the need for accurate measurement of muscle contraction states. Traditional methods, such as electromyography, cannot measure passive muscle contraction states, while optical and ultrasonic techniques suffer from motion artifacts due to their rigid transducers. To overcome these limitations, we developed a flexible multichannel electrical impedance sensor (FMEIS) for noninvasive detection of skeletal muscle contractions. By applying an imperceptible current, the FMEIS can target multiple deep muscles by capturing electric-field ripples generated by their contractions. With an ultrathin profile (~220 micrometers), a low elastic modulus (212.8 kilopascals) closely matching human skin, and engineered adhesive sensor surfaces, the FMEIS conforms nicely to human skin with minimized motion artifacts. The FMEIS achieved high accuracy in both hand gesture recognition and muscle force prediction using machine learning models. With demonstrated performance across multiple HMI applications, including human-robot collaboration, exoskeleton control, and virtual surgery, FMEIS shows great potential for future real-time collaborative HMI systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.