研究業績

Recently, inferring the state of people’s mental health via passive mobile sensing has attracted significant attention. Previous studies have used the self-assessed stress levels as the ground truth; however, these are subjective measures. In this study, we use a physiologically-assessed stress metric to minimize the effect of participant subjectivity and further estimate it using behavioral features based on the smartphone usage logs. We initially requested the study participants (39 participants) to attach heart rate sensors for 8 hours per day and simultaneously collected continuous heart rate data and smartphone logs for 42 days. Further, we divided the participants into four types via clustering using the behavioral features derived from their smartphone sensor logs and trained each model via supervised learning using the heart rate data as the ground truth. Our results exhibit that the proposed method is more accurate (71%) as compared to the baseline method (54%). This demonstrates that physiologically-assessed stress levels can be estimated based on the implicit features that are gathered from the smartphone logs.