ArticleResearch (Washington, D.C.)2024
Deep Learning Model Coupling Wearable Bioelectric and Mechanical Sensors for Refined Muscle Strength Assessment.
Article in Research (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- A Stretchable, Mechanically-Interlocked Polyrotaxane Hydrogel for Wearable Motion and Electrophysiological Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Ultra-stable Flexible Thermal Sensing Operating from 20 to 1273 K Enabled by the Directly Grown MoSmall (Weinheim an der Bergstrasse, Germany) · 2026Article
- Wearable Hybrid Strain-Myoelectric Sensing System for Machine-Learning-Assisted Sarcopenia Screening.Small science · 2026Article
- AI-Empowered Mechanomedicine for Cancer-Related Lymphedema.Research (Washington, D.C.) · 2026Review
- Mantis Leg-Inspired Smart Insole Integrating Closed-Loop Power Supply for Advanced Wearable Gait Diagnostics.Research (Washington, D.C.) · 2026Article
- Wearable Bioelectronics for Home-Based Monitoring and Treatment of Muscle Atrophy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Pulsed laser-assisted direct fabrication of MoMicrosystems & nanoengineering · 2025Article
- Flexible brain electronic sensors advance wearable brain-computer interface.npj biomedical innovations · 2025Review
- Next-Generation Neurotechnologies Inspired by Motor Primitive Model for Restoring Human Natural Movement.Research (Washington, D.C.) · 2025Review
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Muscle strength (MS) is related to our neural and muscle systems, essential for clinical diagnosis and rehabilitation evaluation. Although emerging wearable technology seems promising for MS assessment, problems still exist, including inaccuracy, spatiotemporal differences, and analyzing methods. In this study, we propose a wearable device consisting of myoelectric and strain sensors, synchronously acquiring surface electromyography and mechanical signals at the same spot during muscle activities, and then employ a deep learning model based on temporal convolutional network (TCN) + Transformer (Tcnformer), achieving accurate grading and prediction of MS. Moreover, by combining with deep clustering, named Tcnformer deep cluster (TDC), we further obtain a 25-level classification for MS assessment, refining the conventional 5 levels. Quantification and validation showcase a patient's postoperative recovery from level 3.2 to level 3.6 in the first few days after surgery. We anticipate that this system will importantly advance precise MS assessment, potentially improving relevant clinical diagnosis and rehabilitation outcomes.
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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.