Inferring mood instability via smartphone sensing: A multi-view learning approach

A high correlation between mood instability (MI), the rapid and constant fluctuation in mood, and mental health has been demonstrated. However, conventional approaches to measure MI are limited owing to the high manpower and time cost required. In this paper, we propose a smartphone-based MI detection that can automatically and passively detect MI with minimal human involvement. The proposed method trains a multi-view learning classification model using features extracted from the smartphone sensing data of volunteers and their self-reported moods. The trained classifier is then used to detect the MI of unseen users efficiently, thereby reducing the human involvement and time cost significantly. Based on extensive experiments conducted with the dataset collected from 68 volunteers, we demonstrate that the proposed multi-view learning model outperforms the baseline classifiers.