Advancing Neuromuscular Disorder Diagnosis and Therapy

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This research explores neuromuscular signaling and motor dysfunction across various conditions, including ALS and Parkinson's. It investigates the potential of sEMG for adaptive modeling through a computational framework, utilizing CNN-LSTM and SVM techniques. Data preparation involved extensive sEMG datasets, leading to a robust classification system achieving 96.1% accuracy. Real-time activation detection demonstrated high accuracy with minimal latency. The study also includes biophysical...