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.
A simple Python GUI to explore sensor data and classify emotional states.
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.
Stress detection from wearable HRV signals using a Soft-Voting Ensemble (RF, XGBoost, SVM) on the WESAD dataset. Achieved 82.97% LOSO accuracy.
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