Heart_Rate_Estimation_Using_Wearable_Sensors_and_MachineLearning
Abstract
This research explores the development of a heart rate estimation system that integrates wearable sensors with machine learning techniques to achieve 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.
I (we), the author(s), hereby declare under full moral, financial and criminal liability that the manuscript submitted for publication to the Journal of Computer and Forensic Sciences
a) is the result of my (our) own original research and that I (we) hold the right to publish it;
b) does not infringe any copyright or other third-party proprietary rights;
c) complies with the Journal’s research and publishing ethics standards;
d) has not been published elsewhere, under this or any other title;
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g) that if the article has been accepted for publishing I (we) will transfer all copyright ownership of the manuscript to the University of Criminal Investigation and Police Studies in Belgrade.
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