C# library that handles the full process of gathering biometric data from a body-worn sensor, transforming it into handcrafted feature vectors, and delivering an inferencing result in thirty-five lines of code.
An implementation of the WESAD processing and training pipeline in Python.
An open-source toolkit for auditing wearable physiological signals: signal quality, algorithmic fairness, causal sensitivity, and downstream-task impact.
基于 PPG+EDA 多模态信号的轻量级可穿戴情感状态检测系统 | WESAD | 1D-CNN 三分类 | LOSO | React Dashboard
AI-powered IoT biosensor system for real-time stress detection using ESP32, physiological sensors, Flask, and a Random Forest model trained on the WESAD dataset.
Real-time stress detection wearable using ESP32 + multi-sensor HRV (ECG / PPG / GSR / temp / IMU), SVM trained on WESAD, FastAPI inference service, and React dashboard. LOSO accuracy 0.891 across 16 subjects.
Traceable machine learning on physiological and neural signals: wrist, chest and sleep-EEG pipelines, evaluated subject-wise, with provenance from every prediction back to the raw window.
Operator-state sensing baseline using WESAD Empatica E4 signals, LOSO evaluation, uncertainty flags, and interpretable feedback cards.
An R/Shiny and Python course project with Paula Banach for HRV preprocessing and WESAD-based stress experiments.
Stress detection from wrist signals with 91.33% accuracy. Random Forest on WESAD + DEAP datasets with real-time binaural beat intervention. Python, Scikit-learn, Streamlit.
A simple Python GUI to explore sensor data and classify emotional states.
Multimodal physiological stress recognition on WESAD with LOSO validation and LabVIEW integration
Participant-disjoint WESAD stress-monitoring reliability framework with calibration, threshold, false-alert, robustness, and subject-specific failure audits.
Reproducible Python audit of multimodal WESAD wearable signals and a leakage-aware three-class task definition.
Stress detection from wearable HRV signals using a Soft-Voting Ensemble (RF, XGBoost, SVM) on the WESAD dataset. Achieved 82.97% LOSO accuracy.
StudyBee is an intelligent multimodal learning assistant that combines RAG-based document Q&A, automatic quiz generation, adaptive difficulty control, and real-time emotion recognition (facial, speech, physiological, and text) to deliver a personalized and emotionally-aware study experience.
24/7 multimodal bio-sensing wearable platform: tiered acquisition (PPG/IMU continuous → EEG/fNIRS rest), edge AI triage, LSL/XDF sync, validated on WESAD/MIT-BIH/Sleep-EDF
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