AI RESEARCH
A methodology to rank importance of frequencies and channels in electromyography data with Decision Tree classifiers
arXiv CS.LG
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ArXi:2604.15353v1 Announce Type: cross This study presents a methodology for identifying the most informative frequencies and channels in electromyography (EMG) data to evaluate muscle recovery using Decision Tree classifiers. EMG signals, recorded from the vastus lateralis muscle during squat exercises, were analyzed across varying rest intervals to assess optimal recovery periods.