Robust Heart Rate Estimation Using Wearable Photoplethysmography and Machine Learning Techniques

The Robust Heart Rate Estimation is a study of wearable photoplethysmography. These fresh-out machine lessons aim to find an answer for monitoring heart rate without fuss using non-invasive wearables. It is no more than that, Photoplethysmography heart rate estimation. Train a machine learning model on the PPG signal to estimate heart rate better. Methodology The proposed method is based on extracting some features from the PPG signal, like amplitude, frequency, and waveform characteristics, that can be useful during 3D reconstruction. We then pass these features to a machine learning algorithm for heart rate prediction…. The most commonly used ones are ANNs, SVMs, or random forests. These algorithms have been trained on a dataset of PPG signals and heart rate values to allow accurate predictions. The latter is necessary to maintain a strong perspective, including preprocessing steps introducing white- and pink-colored noise into PPG signal data. It incorporates subject-specific factors such as pigmentation of the skin and movement artifacts to adapt heart rate estimation. This approach can be applied to numerous healthcare and fitness use cases, allowing a convenient, non-intrusive heart rate monitoring solution and user-independent heart rate determination based on wearable technology and machine learning. Combining wearable's and machine learning methods effectively estimates HR accurately and efficiently.