Predicting Your Burnout: Building an HRV Stress Tracker with TCNs and Oura Ring Data

We’ve all been there: waking up feeling like a zombie despite getting eight hours of sleep. While wearables give us data, they often fail to give us foresight. What if you could predict your stress levels 24 hours in advance? 🚀

In this tutorial, we are going to tackle HRV prediction (Heart Rate Variability) using a state-of-the-art Temporal Convolutional Network (TCN). By leveraging the Oura Ring API and deep learning, we’ll transform non-stationary biometric time series into actionable insights. Whether you're into time series forecasting or building the next big health-tech app, mastering Temporal Convolutional Networks (TCN) is a game-changer for handling long-term dependencies without the vanishing gradient headaches of traditional RNNs.

For those looking for more production-ready examples and advanced biometric signal processing patterns, I highly recommend checking out the deep-dives at . They cover how to deploy these models using FastAPI and how to optimize TCNs for mobile edge devices.

Conclusion 🏁

Using Temporal Convolutional Networks for HRV prediction allows us to move beyond reactive data (what happened yesterday) to proactive health management (what will happen tomorrow). TCNs offer a more stable training environment than LSTMs and are significantly faster due to parallelizable convolutions.

What’s next?

1. Multi-modal inputs: Try adding sleep_score and activity_met to the TCN input.


2. Attention mechanisms: Add a self-attention layer after the TCN blocks to weigh certain days (like high-intensity workout days) more heavily.

Did you find this helpful? Drop a comment below with your thoughts on TCN vs. Transformers for time series! 👇