Hand Gesture Recognition via sEMG signals with CNNs (Electrical and Computer Engineering - MSc Thesis)
Machine learning code implemented for hand gesture recognition using EMG data from the Ninapro db1 database. Report Link: https://drive.google.com/file/d/1vHzUKKFz1ifAaLOw91Sb41zo3zN5qRJl/view?usp=sharing
Computationally-free personalization at test time for sEMG gesture classification. Fast (gpu/cpu) ninapro API.
Surface-EMG hand-gesture classification for prosthetic control — a clean, leak-free classical-ML pipeline (NinaPro DB2) with a live demo.
Does training on more subjects fix cross-subject sEMG transfer? A scaling study on Ninapro DB5 — accuracy saturates at four subjects, and model capacity is not the cause.
Minimum sEMG electrode configuration for grip force estimation during assisted grasping with a soft robotic glove, using a physics-informed neural network on Ninapro DB2.
EMG-based hand gesture classification on NinaPro DB5 using PyTorch MLP with SHAP and LIME explainability — identifying which forearm muscles drive each prediction for transparent prosthetic control.
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