Beck_Moulton

Posted on Jun 22

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

#datascience #timeseries #machinelearning #tensorflow

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 WellAlly Blog, which served as a major inspiration for this architecture.

The Architecture: Why TCN?

Traditional LSTMs are great, but they process data sequentially, making them slow and prone to memory loss over long sequences. TCNs, however, use Dilated Causal Convolutions, allowing the model to look back exponentially further into the past with fewer layers.

Data Flow Overview

graph TD
    A[Oura Cloud API] -->|Raw JSON| B(Pandas Preprocessing)
    B -->|Cleaned HRV/Activity| C{Feature Engineering}
    C -->|Sliding Windows| D[TCN Model Training]
    D -->|Dilated Convolutions| E[Stress Trend Prediction]
    E -->|24h Forecast| F[Dashboard/Alerts]
    style D fill:#f9f,stroke:#333,stroke-width:2px

Prerequisites

To follow along, you'll need:

Tech Stack: Python, TensorFlow/Keras, Pandas, Scikit-learn.
Data: An Oura Cloud Personal Access Token (or use the mock data generator provided).
Difficulty: Advanced (Buckle up! 🏎️).

Step 1: Fetching Biometric Data

First, we need to pull our "Readiness" and "Sleep" data. Oura provides high-resolution HRV samples (usually 5-minute intervals during sleep).

import requests
import pandas as pd

def fetch_oura_data(token, start_date, end_date):
    url = f'https://api.ouraring.com/v2/usercollection/daily_readiness'
    headers = {'Authorization': f'Bearer {token}'}
    params = {'start_date': start_date, 'end_date': end_date}

    response = requests.get(url, headers=headers, params=params)
    data = response.json()['data']

    df = pd.DataFrame(data)
    # Extracting HRV (Heart Rate Variability) from the contributors
    df['hrv'] = df['contributors'].apply(lambda x: x.get('hrv_balance'))
    return df[['day', 'hrv']]

# Pro-tip: Handle missing values immediately!
# df['hrv'] = df['hrv'].interpolate(method='time')

Step 2: Building the TCN Layer

The magic of TCN lies in dilation. By increasing the dilation rate, the receptive field grows, allowing the network to "see" days into the past without an explosion of parameters. 🥑

import tensorflow as tf
from tensorflow.keras import layers

def tcn_block(x, filters, kernel_size, dilation_rate):
    # Causal padding ensures we don't cheat by looking into the future
    res = layers.Conv1D(filters, kernel_size, padding='causal', 
                        dilation_rate=dilation_rate, activation='relu')(x)
    res = layers.BatchNormalization()(res)
    res = layers.Dropout(0.2)(res)

    # Residual connection to help with deep gradient flow
    shortcut = layers.Conv1D(filters, 1, padding='same')(x)
    return layers.add([res, shortcut])

def build_tcn_model(input_shape):
    inputs = layers.Input(shape=input_shape)
    x = tcn_block(inputs, 64, 3, 1)
    x = tcn_block(x, 64, 3, 2)
    x = tcn_block(x, 64, 3, 4)
    x = tcn_block(x, 64, 3, 8) # Receptive field grows exponentially!

    x = layers.GlobalAveragePooling1D()(x)
    outputs = layers.Dense(1)(x) # Predicting the next stress value

    model = tf.keras.Model(inputs, outputs)
    model.compile(optimizer='adam', loss='mse', metrics=['mae'])
    return model

Step 3: Training on Non-Stationary Time Series

Biometric data is notoriously noisy. We use Scikit-learn's StandardScaler to normalize our HRV values before feeding them into the TCN.

from sklearn.preprocessing import StandardScaler

# Assume 'data' is our processed HRV sequence
scaler = StandardScaler()
scaled_data = scaler.fit_transform(data[['hrv']])

# Create sliding windows (e.g., use past 7 days to predict next day)
def create_windows(data, window_size=7):
    X, y = [], []
    for i in range(len(data) - window_size):
        X.append(data[i:i+window_size])
        y.append(data[i+window_size])
    return np.array(X), np.array(y)

X_train, y_train = create_windows(scaled_data)
model = build_tcn_model((7, 1))
model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2)

The "Official" Way to Scale

While this DIY approach is great for learning, production-level health tracking requires handling edge cases like sensor disconnection, irregular sampling, and multi-modal fusion (combining HRV with sleep stages and activity).

For a deeper dive into Production-grade Health AI and more advanced "Learning in Public" modules, you absolutely must check out the resources at WellAlly Tech Blog. 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?

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

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