DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection

Abstract

The increasing use of electronic forms of communication presents new opportunities in the study of mental health, including the ability to investigate the manifestations of psychiatric diseases unobtrusively and in the setting of patients' daily lives. A pilot study to explore the possible connections between bipolar affective disorder and mobile phone usage was conducted. In this study, participants were provided a mobile phone to use as their primary phone. This phone was loaded with a custom keyboard that collected metadata consisting of keypress entry time and accelerometer movement. Individual character data with the exceptions of the backspace key and space bar were not collected due to privacy concerns. We propose an end-to-end deep architecture based on late fusion, named DeepMood, to model the multi-view metadata for the prediction of mood scores. Experimental results show that 90.31% prediction accuracy on the depression score can be achieved based on session-level mobile phone typing dynamics which is typically less than one minute. It demonstrates the feasibility of using mobile phone metadata to infer mood disturbance and severity.

KDD 2017

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Cited by in corpus (17)

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• Learning from Multi-View Multi-Way Data via Structural Factorization Machines
• Private Model Compression via Knowledge Distillation
• Deep Learning Towards Mobile Applications
• Multimodal Privacy-preserving Mood Prediction from Mobile Data: A Preliminary Study
• MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation
• dpMood: Exploiting Local and Periodic Typing Dynamics for Personalized Mood Prediction
• Transformer-Based Behavioral Representation Learning Enables Transfer Learning for Mobile Sensing in Small Datasets
• From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science
• Broad Learning for Healthcare
• Neural Learning of Online Consumer Credit Risk