Heart Rate Extraction from Photoplethysmography Signal: A Multi Model Machine Learning Approach

The purpose of this research is to estimate the heart rate (HR) from wearable gadgets, for example, fingertip gadgets. As the skin of finger-tip is slight, it is not difficult to separate pulse from that point. An optimistic component in this day, HR checking is Photoplethysmography (PPG). Moreover, during physical workout HR extraction precision is truly influenced by clamor and movement artifact (MA). To extract HR variability there are numerous ordinary techniques. In this research, a novel way is utilized to extract HR which is known as a multi-model machine learning technique. In this study, initially training and testing of our developed algorithm is done for various features and various dataset. In addition, separation of noisy and non noisy information is done by K means clustering. Then, the machine gain information from noisy and non noisy dataset. The Linear Regression model is utilized to estimate HR by using dataset. In this study, the feature engineering is also done, as it were, we choose an alternate set of features and know their conduct with our recommended technique and we discover error percentage for each set of features. There were 12 subject from where trial dataset were recorded. The root mean square (RMS) and the mean absolute error of HR was extracted. The lowest absolute mean error we find in this research is 3.06 beats per minute (BPM).