surface-emg

This repository shows how to classify EMG data coming from Myo Armband using neural networks and interface a 3d printed robotic hand to imitate the classified movement

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

Gesture Recognition by Instantaneous Surface EMG Images

Surface EMG-based Inter-session Gesture Recognition Enhanced by Deep Domain Adaptation

Implementation of surface EMG decomposition as proposed on Francesco Negro et al 2016 J. Neural Eng. 13 026027.

Surface electromyography based gesture recognition based on dual-stream CNN

Wireless ESP32 + AD8232 surface EMG system with real-time WiFi streaming, OTA updates, automatic calibration, and a lightweight Python GUI for live signal monitoring.

Semi-supervised Learning for Surface EMG-based Gesture Recognition

Progressive Fusion Network (PFNet) for Integration of Domain Knowledge Guided Feature Engineering and Deep Feature Learning in Surface Electromyography Based Hand Movement Recognition

Hierarchical View Pooling Network (HVPN) for Surface Electromyography Based Hand Movement Recognition

Multimodal sensor fusion (sEMG, AMG, Kinematics) & normative Isolation Forest anomaly detection for tracking age-associated functional movement degradation in Sit-to-Walk transitions. Features participant-disjoint exercise classification, 100-resample outlier analysis, and covariate-adjusted LMMs across young, middle, and older adult cohorts.

Surface EMG-based fatigue analysis using RMS and MDF biomarkers