Hand Gesture Recognition via sEMG signals with CNNs (Electrical and Computer Engineering - MSc Thesis)

The source code for the real-time hand gesture recognition algorithm based on Temporal Muscle Activation maps of multi-channel surface electromyography (sEMG) signals (ICASSP 2021)

Accompaniment code for 'Hilbert sEMG data scanning for hand gesture recognition based on Deep Learning' published in NCAA.

Source code for multiple parameter modelling of synthetic electromyography data.

Biomedical signal (EEG/sEMG/ECG) completion/imputation using diffusion model. "A robust denoising diffusion framework for completing missing regions of multiple biomedical signals"

Computationally-free personalization at test time for sEMG gesture classification. Fast (gpu/cpu) ninapro API.

Auto-learning search framework based on a weighted double Q-learning algorithm:"Integrated block-wise neural network with auto-learning search framework for finger gesture recognition using sEMG signals"

Python algorithm to assess muscle activation patterns during cyclical movements

Robust sEMG-based hand gesture recognition using deep learning and multi-feature signal processing.

PyTorch design-space study: 5 sequence models across 3 prediction horizons for sEMG-based variable-impedance teleoperation latency compensation (synthetic data).

Bachelor Thesis work developed in 2025 at University of Bologna. See README for more infos about the project.

Real time sEMG gesture recognition for CNN based vehicle control in CARLA.

Surface-EMG hand-gesture classification for prosthetic control — a clean, leak-free classical-ML pipeline (NinaPro DB2) with a live demo.

Evaluation protocols matter: quantifying protocol sensitivity in sEMG CNNs