Artificial neural networks-based classification of emotions using wristband heart rate monitor data

Heart rate variability (HRV) is an objective measure of emotional regulation. This study aimed to estimate the accuracy with which an artificial neural network (ANN) algorithm could classify emotions using HRV data that were obtained using wristband heart rate monitors. Four emotions were evoked during gameplay: pleasure, happiness, fear, and anger. Seven normalized HRV features (i.e., 3 time-domain features, 3 frequency-domain features, and heart rate), which yielded 29,727 segments during gameplay, were collected and analyzed first by statistics and then classified by the trained ANN model. General linear model adjusted for individual differences in HRV showed that all HRV features significantly differed across emotions, despite disparities in their magnitudes and associations. When compared to neutral status (i.e., no emotion evoked), the mean of R-R interval was significantly higher for pleasure and fear but lower for happiness and anger. In addition, pleasure evidenced the HRV features that suggested a superior parasympathetic to sympathetic activation. Happiness was associated with a prominent sympathetic activation. These statistical findings suggest that HRV features significantly differ across emotions evoked by gameplay. When further utilizing ANN-based emotion classification, the accuracy rates for prediction were above 75.0% across the 4 emotions with accuracy rates for classification of paired emotions ranging from 82.0% to 93.4%. For classifying emotion in an individual person, the trained ANN model utilizing HRV features yielded a high accuracy rate in our study. ANN is a time-efficient and accurate means to classify emotions using HRV data obtained from wristband heart rate monitors. Thus, this integrated platform can help monitor and quantify human emotions and physiological biometrics.