Building a Transformer-based HRV anomaly detection engine with TensorFlow and InfluxDB

Short summary

This advanced tutorial walks through building a real-time Heart Rate Variability anomaly detection engine using a Time-Series Transformer architecture in TensorFlow. It covers the full pipeline: ingesting wearable data via MQTT, storing in InfluxDB, running Transformer-based inference, and visualizing alerts in Grafana. Code snippets are provided for the MQTT-to-InfluxDB bridge and the multi-head attention Transformer encoder model.

• •Full pipeline: wearable data → MQTT → InfluxDB → TensorFlow Transformer → Grafana alerts
• •Transformer encoder architecture with multi-head attention captures long-range physiological dependencies
• •Includes working Python code for MQTT ingestion and model construction

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