Estimating Physical/Mental Health Condition Using Heart Rate Data from a Wearable Device.

We propose an estimation method of subjects' physical/mental health condition from their heart rate (HR) and evaluate it on the newly collected data including 25 million points over 97 participants. The accurate health condition estimation is important for an employee's mental health care and an objective understanding of our condition. For the estimation, the heart rate variability (HRV) has been widely used, but there are some technical difficulties with measuring the HRV, such as maintaining a good quality of data for a long period of time. Here, we predict the subjects' physical/mental health only from the HR measured by Fitbit instead of the HRV. We first measured more than 25 million points of HR and steps data from 97 participants over 3 months using the Fitbit Inspire HR. We also conducted questionnaires to check their physical conditions each day. We then predict their condition by focusing on the inactive period of HR and applying the support vector machine to the preprocessed data. The best balanced accuracy of our method achieved 0.582, which was higher than the state-of-the-art method with HRV whose accuracy is 0.565.