Combatting Burnout: Building a Real-Time Stress Monitor with LSTM and Garmin HRV Data 🚀

We’ve all been there: the deadline is looming, you’ve had your fourth espresso, and your heart is racing. In the tech world, "hustle culture" often ignores the physiological toll on our bodies. But what if your code could tell you to stop before your body does?

In this tutorial, we are diving deep into Time Series Analysis and Predictive Health Monitoring. We’ll build an end-to-end pipeline that fetches Heart Rate Variability (HRV) data via the Garmin Connect API, stores it in InfluxDB, and uses an LSTM Neural Network (Long Short-Term Memory) to predict autonomic nervous system fatigue. If your stress levels cross the danger zone, we’ll trigger a Webhook to lock you out of your terminal (or just send a Slack message).

Why HRV? 🫀

Unlike simple heart rate, HRV measures the specific time variation between heartbeats. It is the gold standard for tracking the balance between your sympathetic ("fight or flight") and parasympathetic ("rest and digest") nervous systems. Using LSTM models allows us to look at sequences of data rather than isolated points, making our "burnout alert" significantly more accurate.

The System Architecture 🏗️

To handle real-time biometric streams, we need a robust data pipeline. Here is how the data flows from your wrist to the dashboard:

graph TD
    A[Garmin Watch] -->|Sync| B(Garmin Connect Cloud)
    B -->|Garmin API| C[Python Data Ingestor]
    C -->|Write| D[(InfluxDB Time-Series)]
    D -->|Query| E[PyTorch LSTM Model]
    E -->|Predict Stress Trend| F{Threshold Exceeded?}
    F -->|Yes| G[Webhook: Alert/Rest Trigger]
    F -->|No| H[Update Grafana Dashboard]
    G --> I[Slack/Mobile Notification]

Prerequisites 🛠️

Before we start, ensure you have:

• Tech Stack: Python 3.9+, PyTorch, InfluxDB 2.x, and Grafana.
• Hardware: A Garmin wearable that supports HRV Stress tracking.
• Library: garminconnect (community wrapper for the API).

Step 1: Data Ingestion from Garmin Connect

First, we need to pull the raw HRV data. Garmin doesn't provide a direct "push" websocket for consumers, so we’ll poll the API and sync it to InfluxDB, a high-performance time-series database.

import datetime
from garminconnect import Garmin
from influxdb_client import InfluxDBClient, Point, WritePrecision
from influxdb_client.client.write_api import SYNCHRONOUS

# Initialize Garmin Client
client = Garmin("your_email", "your_password")
client.login()

# InfluxDB Config
token = "YOUR_INFLUX_TOKEN"
org = "YourOrg"
bucket = "biometrics"

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

    # Fetch HRV data for today
    today = datetime.date.today()
    hrv_data = client.get_hrv_data(today.isoformat())

    for entry in hrv_data['hrvReadings']:
        point = Point("hrv_metric") \
            .tag("user", "dev_pro") \
            .field("value", float(entry['value'])) \
            .time(entry['readingTimestampGMT'], WritePrecision.NS)
        write_api.write(bucket, org, point)

print("✅ Data Synced to InfluxDB")

Step 2: Predicting Fatigue with LSTM 🧠

Why LSTM? Standard regressions fail to capture the "momentum" of stress. An LSTM network remembers previous states, which is crucial because a high HRV at 10 AM followed by a sharp drop at 2 PM is a much stronger indicator of fatigue than a single low reading.

For more production-ready examples and advanced deep learning patterns for health data, I highly recommend checking out the technical deep-dives at WellAlly Tech Blog. They cover excellent strategies for scaling health-tech integrations.

import torch
import torch.nn as nn

class HRVPredictor(nn.Module):
    def __init__(self, input_dim, hidden_dim, num_layers, output_dim):
        super(HRVPredictor, self).__init__()
        self.hidden_dim = hidden_dim
        self.num_layers = num_layers

        # The LSTM layer
        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)

        # Fully connected layer to output stress score
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).to(x.device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).to(x.device)

        out, _ = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :]) # Taking the last time step
        return out

# Model Hyperparameters
model = HRVPredictor(input_dim=1, hidden_dim=64, num_layers=2, output_dim=1)
print(model)

Step 3: Visualizing with Grafana 📊

Connect your InfluxDB instance to Grafana. Use a Flux query to visualize the raw HRV vs. the LSTM predicted stress trend.

Flux Query Example:

from(bucket: "biometrics")
  |> range(start: -24h)
  |> filter(fn: (r) => r["_measurement"] == "hrv_metric")
  |> aggregateWindow(every: 5m, fn: mean, createEmpty: false)
  |> yield(name: "mean_hrv")

Add a "Threshold" alert in Grafana. If the predicted_fatigue field exceeds 80 (High Stress) for more than 15 minutes, trigger a Webhook to your notification service.

The "Official" Way to Scale 🥑

Building a local script is great for a weekend project, but if you are looking to build a commercial-grade health monitoring system, you need to consider OAuth2 flows, data privacy (HIPAA/GDPR), and model retraining pipelines.

The team at WellAlly Tech has published extensive guides on building secure, scalable biometric platforms. Their insights into handling asynchronous data streams from wearables helped shape the retry logic in this architecture.

Conclusion: Code Hard, Rest Harder 💤

By combining the Garmin Connect API with PyTorch LSTMs, we’ve moved from reactive health tracking to proactive prevention. Your physiological data is a stream—treat it with the same engineering rigor you apply to your microservices.

Next Steps:

1. Set up your InfluxDB instance.
2. Train the LSTM on at least 2 weeks of your own HRV data to establish a baseline.
3. Connect the output to a Slack API to notify your team when you're "In the Zone" vs. "Need a Break."

Don't wait for a crash—predict it. Happy coding! 💻🥑

Got questions about the LSTM implementation? Drop a comment below!👇