Shurun-Wang/MFTCAN-KNR
Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based Network
This tool helps researchers and clinicians accurately track human joint movements, like elbow or knee bends, by interpreting signals from muscles. It takes raw surface electromyography (sEMG) data, which measures muscle electrical activity, and converts it into a continuous stream of predicted joint angles. Physical therapists, sports scientists, or biomechanics researchers would use this to understand movement patterns without invasive sensors.
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Use this if you need a non-invasive way to continuously estimate joint angles from muscle electrical signals for research or rehabilitation purposes.
Not ideal if you require real-time, high-precision control for prosthetics or exoskeletons, as this is primarily a research tool for movement analysis.
Stars
14
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1
Language
Python
License
MIT
Category
Last pushed
Dec 19, 2023
Commits (30d)
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