ArticleScientific reports2025
Deep learning for motion classification in ankle exoskeletons using surface EMG and IMU signals.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
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Who cites it
4 citing papers in PubMed.
- Real-Time Prediction of Lower-Limb Joint Kinematics, Kinetics, and Ground Reaction Force Using Wearable Sensors and Machine Learning.Sensors (Basel, Switzerland) · 2026Article
- Lower Limb Motion Classification of Actions in Confined Environments Based on Multi-Source Signal Fusion and Muscle Symmetry Features.Sensors (Basel, Switzerland) · 2026Article
- Seamless Human-Computer Interaction Enabled by Wearable Biointerfaces and Intelligent Systems.Biomimetics (Basel, Switzerland) · 2026Review
- Adaptive ensemble sizing with reinforcement learning for real-time ankle injury detection in wearable sensor systems.BMC sports science, medicine & rehabilitation · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Ankle exoskeletons have garnered considerable interest for their potential to enhance mobility, support rehabilitation, and reduce fall risks, particularly among the aging population. Their effectiveness depends on accurate, real-time prediction of user intentions from wearable sensor data, as even small delays or errors can compromise stability and safety. Here, we present a motion classification framework that integrates three Inertial Measurement Units (IMUs) with eight surface Electromyography (sEMG) sensors fabricated as towel-based textile electrodes, which improve comfort, durability, and usability for long-term deployment compared to traditional gel electrodes. The dataset comprises multichannel time-series recordings of five functional daily motions, enabling a realistic evaluation of exoskeleton use in everyday environments. Using this framework, Convolutional Neural Networks (CNNs) achieved an accuracy of
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Registered trials
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