A Machine Learning Approach for Heart Rate Estimation from PPG Signal using Random Forest Regression Algorithm

In this paper, a new method is proposed to estimate the heart rate (HR) from wearable devices. A promising feature in today's world of HR monitoring is Photoplethysmography (PPG). However, during physical exercise HR estimation accuracy is seriously affected by noise and motion artifacts (MA). To reduce the effect of MA and estimate HR changes there are many conventional algorithms. Here, a new approach to estimate HR which is called multi-model machine learning approach (MMMLA) is shown. In this proposed algorithm, it firstly trains and tests the model for the different feature and different data set. Then it separates noisy and non-noisy data by K-means clustering. This lets the machine learn from noisy and non-noisy data. Then the Random Forest Regression algorithm is used to fit data and predict HR from test data. Here, feature engineering is also done, in other words, a different set of the feature is chosen and check their behavior with our proposed model and the error rate for every set of the feature was calculated. The mean absolute error and root mean square (RMS) error of HR was calculated. The lowest mean absolute error found in this research was 1.11 beats per minute (BPM). This result shows the capability of proposed machine learning-empowered system in HR estimation from PPG signal.