realmichaelye/Stress-Prediction-Using-HRV
Using the SWELL dataset from Kaggle, we've built 2 machine learning models to predict whether or not a person is under stress using Heart Rate Variability(HRV) which can be collected from modern wearables such as fitbit devices and apple watches.
The project implements a feed-forward neural network (34→10→3 neurons with ReLU/softmax) alongside a KNeighbors classifier to enable real-time stress detection from wearable HRV data. Beyond classification, it targets practical biofeedback applications—triggering notifications, launching meditation apps, or automating ambient responses like playing calming music through smart home devices. The architecture emphasizes low-latency inference suitable for on-device or edge deployment on wearables and smartphones.
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Sep 28, 2019
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