First‑year BTech Electrical Engineering project (2020–21): NI Multisim simulation of a wearable stress‑meter with sensor‑fusion analytics.

This repository offers a pipeline for classifying insomnia using EEG, EMG, EOG, and ECG signals, featuring early and late fusion, signal preprocessing, feature extraction, and machine learning models for accurate detection.

Stress detection using physiological signals from the WESAD dataset. Built with Python for time series analysis, feature extraction, and machine learning. Ideal for health tech and wearable applications.

This repository implements a fusion algorithm based on a constant velocity model to improve the accuracy of saccade parameter measurements using electrooculography (EOG) signals. By combining regression-based and threshold-based estimations, the method enhances the detection of saccade amplitude, velocity, and duration.

stema de Aquisição e Classificação de Sinais EMG para Controle de Próteses Mioelétricas. Projeto baseado em Arduino e machine learning para leitura, processamento e identificação de movimentos musculares via sensores EMG, com foco em aplicações assistivas e controle de próteses de mão.