DEV Community

Beck_Moulton
Beck_Moulton

Posted on

Stop Burning Out: Using XGBoost and HRV Data to Predict Physical Exhaustion

#machinelearning #datascience #python #wearables

Are you pushing your limits at the gym, or is that morning double-espresso just masking a deeper physiological fatigue? In the world of high-performance athletics and high-stress software engineering, knowing when to rest is just as important as knowing when to grind.

In this tutorial, we are diving deep into time-series forecasting and predictive analytics to transform raw wearable data into a burnout early-warning system. By leveraging Heart Rate Variability (HRV) data from devices like Apple Watch or Oura Ring, we will build a machine learning pipeline using XGBoost and InfluxDB to predict "overstrain" states before they manifest as illness or injury.

The Architecture of Health Intelligence

To build a robust prediction model, we need a pipeline that handles high-velocity biometric data, performs complex feature engineering, and provides low-latency inference.

graph TD
    A[Wearable Device: Apple Health/Oura] -->|Raw HRV/R-R Intervals| B(Data Ingestion API)
    B --> C{InfluxDB}
    C -->|Time-Series Queries| D[Feature Engineering Engine]
    D -->|Time/Frequency Domain Metrics| E[XGBoost Model]
    E --> F{Burnout Risk Score}
    F -->|High Risk| G[Mobile Notification/Alert]
    F -->|Low Risk| H[Continue Training]

    subgraph "Feature Extraction"
    D1[SDNN]
    D2[RMSSD]
    D3[Moving Averages]
    end
Enter fullscreen mode Exit fullscreen mode

Prerequisites

Before we start coding, ensure you have the following stack ready:

  • Python 3.9+
  • Pandas & Scikit-learn: For data manipulation.
  • XGBoost: Our primary gradient boosting framework.
  • InfluxDB: To store and query time-series biometric data.
  • Wearable Data: Exported CSV or JSON from HealthKit or Oura Cloud API.

Step 1: Data Ingestion & Storage with InfluxDB

Wearable data is inherently temporal. While a CSV works for experiments, a production-grade system needs a time-series database. We'll use InfluxDB to store HRV readings (measured in milliseconds).

import pandas as pd
from influxdb_client import InfluxDBClient, Point, WritePrecision
from influxdb_client.client.write_api import SYNCHRONOUS

# Initialize InfluxDB Connection
token = "YOUR_TOKEN"
org = "Your_Org"
bucket = "biometrics"

client = InfluxDBClient(url="http://localhost:8086", token=token, org=org)
write_api = client.write_api(write_options=SYNCHRONOUS)

def upload_hrv_data(df):
    for index, row in df.iterrows():
        point = Point("heart_rate_variability") \
            .tag("user_id", "dev_user_01") \
            .field("ms", float(row['hrv_value'])) \
            .time(row['timestamp'], WritePrecision.NS)
        write_api.write(bucket, org, point)

print("✅ Data successfully synced to InfluxDB")
Enter fullscreen mode Exit fullscreen mode

Step 2: Advanced Feature Engineering (The Secret Sauce)

Raw HRV numbers mean little without context. To predict burnout, we need to extract features like RMSSD (Root Mean Square of Successive Differences) and SDNN (Standard Deviation of NN intervals).

import numpy as np

def extract_features(data):
    # Rolling window of 7 days to capture baseline
    data['rolling_rmssd_7d'] = data['hrv'].rolling(window=7).mean()
    data['hrv_velocity'] = data['hrv'].diff() # Rate of change

    # Identify "Stress" events (e.g., HRV drops 20% below baseline)
    data['is_strained'] = np.where(data['hrv'] < (data['rolling_rmssd_7d'] * 0.8), 1, 0)

    # Lag features to help XGBoost see the 'trend'
    for i in range(1, 4):
        data[f'hrv_lag_{i}'] = data['hrv'].shift(i)

    return data.dropna()

# Example usage
# df = pd.read_csv('hrv_export.csv')
# processed_df = extract_features(df)
Enter fullscreen mode Exit fullscreen mode

Step 3: Building the XGBoost Overstrain Predictor

XGBoost is excellent for tabular time-series data because it captures non-linear relationships between "yesterday's sleep," "today's HRV," and "tomorrow's exhaustion risk."

import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

# Prepare Features and Target
X = processed_df.drop(['is_strained', 'timestamp'], axis=1)
y = processed_df['is_strained']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize Model
model = xgb.XGBClassifier(
    n_estimators=100,
    max_depth=5,
    learning_rate=0.1,
    objective='binary:logistic',
    use_label_encoder=False
)

model.fit(X_train, y_train)

# Evaluation
preds = model.predict(X_test)
print(classification_report(y_test, preds))
Enter fullscreen mode Exit fullscreen mode

The "Official" Way: Advanced Patterns & Production Ready Models

While this tutorial provides a solid foundation for local development, scaling health-tech applications requires handling data privacy (HIPAA/GDPR), real-time anomaly detection, and cross-device calibration.

For a deeper dive into production-grade health data pipelines and advanced LSTM-based time-series patterns, check out the engineering deep-dives at WellAlly Tech Blog. They cover everything from medical-grade signal processing to deploying ML models at the edge.

Step 4: Visualizing the Fatigue Forecast

Finally, we want to visualize our predictions. A significant drop in the predicted HRV baseline indicates a need for a "De-load" week.

import matplotlib.pyplot as plt

plt.figure(figsize=(12, 6))
plt.plot(processed_df['timestamp'], processed_df['hrv'], label='Actual HRV')
plt.plot(processed_df['timestamp'], processed_df['rolling_rmssd_7d'], label='7D Baseline', linestyle='--')
plt.fill_between(processed_df['timestamp'], 0, 1, where=processed_df['is_strained']==1, 
                 color='red', alpha=0.3, transform=plt.gca().get_xaxis_transform(), label='Predicted Strain')
plt.title("Burnout Warning System: HRV vs. Predicted Strain")
plt.legend()
plt.show()
Enter fullscreen mode Exit fullscreen mode

Conclusion

Predicting burnout isn't magic—it's math. By combining the temporal storage power of InfluxDB with the predictive prowess of XGBoost, you can turn your Apple Watch into a sophisticated health coach.

Next Steps:

  1. Try adding "Sleep Quality" or "Step Count" as additional features.
  2. Experiment with LSTM (Long Short-Term Memory) networks if you have more than 6 months of data.
  3. Implement a feedback loop to retrain the model as your fitness levels improve!

What are you building with your health data? Let me know in the comments! 👇

Top comments (0)