In the world of wearable health technology, the holy grail has always been moving intelligence from the cloud to the edge. Waiting for a cloud server to analyze your heart rhythm is not just a latency issue—it's a privacy and battery life concern. Today, we are diving deep into TinyML, Edge AI, and ECG signal processing to build a real-time abnormality detector.
By leveraging TensorFlow Lite for Microcontrollers and the versatile ESP32, we can process raw electrocardiogram (ECG) data locally. This approach ensures low-latency detection of arrhythmias while keeping sensitive medical data on-device. If you've been looking to bridge the gap between high-level deep learning and low-level embedded systems, you're in the right place!
The Architecture: From Raw Signal to Insight 🏗️
The pipeline involves capturing a high-frequency analog signal, cleaning it, and feeding it into a quantized Convolutional Neural Network (CNN). Here is how the data flows through our ESP32:
graph TD
