Smartphone-based mental state estimation: A survey from a machine learning perspective

Abstract

Monitoring mental health has received considerable attention as a countermeasure against the increasing occurrence of mental illness worldwide. However, current monitoring services incur costs because users are required to attach wearable devices or answer questions. To reduce such costs, many studies have used smartphone-based passive sensing technology to capture a user’s mental state. This paper reviews those studies from the perspective of machine learning and statistical analysis. Forty-four studies published since 2011 have been reviewed and summarized from three perspectives: designed features, machine learning algorithm, and evaluation method. The features considered include location and mobility, activity, speech, sleep, phone usage, and context features. Tasks are classified as correlation analysis, regression tasks, and classification tasks. The machine learning algorithm used for each task is summarized. Evaluation metrics and cross validation methods are also summarized. For those who are not necessarily machine learning experts, we aim to provide information on typical machine learning framework for smartphone-based mental state estimation. For experts in the field, we hope this review will be a helpful tool to check for potential omissions.