Heart rate estimation using wearable sensors and machine learning

This research explores the development of a heart rate estimation system that integrates wearable sensors with machine learning techniques to achieve high accuracy, low cost, and real-time performance. The project aims to build two-stage phases: a software pipeline for model training and a hardware framework for real-world testing. In the first phase, various machine learning algorithms are trained and fine-tuned using the publicly available PPG DaLiA dataset, which contains physiological data collected during everyday activities. The training process focuses on optimizing performance across different model architectures and configurations. The second phase involves implementing the trained model on a real-time embedded system. An ESP32 microcontroller serves as the central unit to collect data from multiple sensors, including electrocardiography (ECG), photoplethysmography (PPG), galvanic skin response (GSR), temperature, and a 3-axis accelerometer. This data is transmitted wirelessly for preprocessing and inference. The user will see their final predicted heart rate on both an OLED display and a user interface (UI) dashboard.