Predicting and Classifying Heart Rates Using Instantaneous Video Data

• Published in: 2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC) pp. 1076 - 1083
• Main Authors: Upama, Paramita Basak, Rabbani, Masud, Alam, Kazi Shafiul, He, Lin, Tian, Shiyu, Syam, Mohammad, Iqbal, Iysa, Kolli, Anushka, Kolli, Hansika, Shefa, Syeda, Sobhani, Bipasha, Ahamed, Sheikh Iqbal
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2023
• Subjects: Autoregressive integrated moving average (ARIMA); Data models; Detection and Record Electronically Acceptable Medical Data (dDream); Exponential Triple Smoothing (ETS); Predicting heart rate; Prediction algorithms; Predictive models; Smoothing methods; Time measurement; Time series analysis; Wearable computers
• DOI: 10.1109/COMPSAC57700.2023.00163

Heart Rate (HR) and Heart Rate Variability (HRV) is an essential measurement to know the heart's cardiovascular condition. Many works have been done for measuring HR-HRV based on the facial video non-invasively. In this paper, based on our previous work experience of measuring HR-HRV by Remote photoplethysmography signals (rPPG) analysis, we have built a prediction model from the 10-second time series data extracted from a facial video. In this work, we have used the instantaneous public dataset with several data models to predict the HR-HRV, and stress levels exclusively from the dataset. We have used here some of the popular algorithms appropriate for this task. We have also analyzed the stress level classification on the gender of a subject using the same facial videos with 16 different classifiers resulting in almost perfect accuracy for several classifiers.