research-article

Prediction of Mood Instability with Passive Sensing

Authors:

Mehrab Bin Morshed,

Sidney K. D'Mello,

Munmun De Choudhury,

Gregory D. Abowd,

Thomas PlötzAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 3

Article No.: 75, Pages 1 - 21

https://doi.org/10.1145/3351233

Published: 09 September 2019 Publication History

Abstract

Mental health issues, which can be difficult to diagnose, are a growing concern worldwide. For effective care and support, early detection of mood-related health concerns is of paramount importance. Typically, survey based instruments including Ecologically Momentary Assessments (EMA) and Day Reconstruction Method (DRM) are the method of choice for assessing mood related health. While effective, these methods require some effort and thus both compliance rates as well as quality of responses can be limited. As an alternative, We present a study that used passively sensed data from smartphones and wearables and machine learning techniques to predict mood instabilities, an important aspect of mental health. We explored the effectiveness of the proposed method on two large-scale datasets, finding that as little as three weeks of continuous, passive recordings were sufficient to reliably predict mood instabilities.

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Index Terms

Prediction of Mood Instability with Passive Sensing
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Empirical studies in ubiquitous and mobile computing
    2. Ubiquitous and mobile computing design and evaluation methods

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 3, Issue 3

September 2019

1415 pages

EISSN:2474-9567

DOI:10.1145/3361560

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Copyright © 2019 ACM.

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Published: 09 September 2019

Published in IMWUT Volume 3, Issue 3

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https://doi.org/10.1145/3712299
Thukral MHaresamudram HPlötz T(2025)Cross-Domain HAR: Few-Shot Transfer Learning for Human Activity RecognitionACM Transactions on Intelligent Systems and Technology10.1145/370492116:1(1-35)Online publication date: 20-Jan-2025
https://dl.acm.org/doi/10.1145/3704921
Arbaizar LLopez-Castroman JArtés-Rodríguez AOlmos PRamírez D(2024)Emotion Forecasting: A Transformer-Based Approach (Preprint)Journal of Medical Internet Research10.2196/63962Online publication date: 5-Jul-2024
https://doi.org/10.2196/63962
Song MYang ZTriantafyllopoulos AZhang ZNan ZTang MTakeuchi HNakamura TKishi AIshizawa TYoshiuchi KSchuller BYamamoto Y(2024)Empowering Mental Health Monitoring Using a Macro-Micro Personalization Framework for Multimodal-Multitask Learning: Descriptive StudyJMIR Mental Health10.2196/5951211(e59512)Online publication date: 18-Oct-2024
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