Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients
Abstract
Today's health care is difficult to imagine without the possibility to objectively measure various physiological parameters related to patients' symptoms (from temperature through blood pressure to complex tomographic procedures). Psychiatric care remains a notable exception that heavily relies on patient interviews and self-assessment. This is due to the fact that mental illnesses manifest themselves mainly in the way patients behave throughout their daily life and, until recently there were no "behavior measurement devices." This is now changing with the progress in wearable activity recognition and sensor enabled smartphones. In this paper, we introduce a system, which, based on smartphone-sensing is able to recognize depressive and manic states and detect state changes of patients suffering from bipolar disorder. Drawing upon a real-life dataset of ten patients, recorded over a time period of 12 weeks (in total over 800 days of data tracing 17 state changes) by four different sensing modalities, we could extract features corresponding to all disease-relevant aspects in behavior. Using these features, we gain recognition accuracies of 76% by fusing all sensor modalities and state change detection precision and recall of over 97%. This paper furthermore outlines the applicability of this system in the physician-patient relations in order to facilitate the life and treatment of bipolar patients.
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Prior investigations have primarily focused on detecting presence or absence of depression rather than on assessing its severity, and they have shown overall low accuracy.
“This has resulted in possible reliability problems, since these investigations have primarily focused on detecting presence or absence of depression rather than on assessing its severity, and they have shown overall low accuracy (14–18, 23–27).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [17]
Severe depressive symptoms are associated with shorter duration of incoming and outgoing calls, and fewer incoming and outgoing phone calls and daily text messages.
“recent work shows that severe depressive symptoms are associated with shorter duration of incoming and outgoing calls, and fewer incoming and outgoing phone calls and daily text messages (13–18).”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [17]
Studies conducted with patients with depression have mostly relied on self-report questionnaires to assign diagnoses and/or measure variation in symptoms and train the models.
“Those studies conducted with patients with depression have mostly relied on self-report questionnaires to assign diagnoses and/or measure variation in symptoms and train the models.”
From Monitoring Changes in Depression Severity Using Wearable and Mobile Sensors (2020) · cites this paper as [17]
A growing body of research has demonstrated the potential of mobile phone sensors to detect a variety of behaviors related to depression, such as activity, sleep, and social interactions.
“A growing body of research has demonstrated the potential of mobile phone sensors to detect a variety of behaviors related to depression, such as activity, sleep, and social interactions (Abdullah et al., 2014; Grunerbl et al., 2015; Kwapisz, Weiss & Moore, 2011; Min et al., 2013; Shoaib et al., 2014; Thomée, Härenstam & Hagberg, 2011; Wiese et al., 2015).”
From The relationship between mobile phone location sensor data and depressive symptom severity (2016) · cites this paper as Grunerbl et al., 2015
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References 31 · Cited by 384
- Authors
- Grunerbl, A., Muaremi, A., Osmani, V., Bahle, G., Ohler, S., Troster, G., Mayora, O., Haring, C., & Lukowicz, P.
- Affiliations
- ETH Zurich (CH)
- FBK CREATE-NET Research Center (IT)
- German Research Centre for Artificial Intelligence (DE)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2015-01
- Volume
- 19
- Issue
- 1
- Pages
- 140-148
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2005069458
- Cited by
- 384
- References
- 31
- Citations by year
- 2026: 16 · 2025: 20 · 2024: 21 · 2023: 29 · 2022: 30 · 2021: 45 · 2020: 43 · 2019: 34 · 2018: 45 · 2017: 38 · 2016: 35 · 2015: 25 · 2014: 3
- FWCI
- 30.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- MONARCA Project
- EU FP7
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Keywords
- bipolar disorder, smartphone sensing, state recognition, state change detection, depression, mania, wearable activity recognition, multimodal sensor fusion, behavior measurement, remote patient monitoring, physician-patient relationship
- MeSH terms
- Algorithms, Bipolar Disorder, Diagnosis, Computer-Assisted, Humans, Sensitivity and Specificity, User-Computer Interface, Reproducibility of Results, Telemedicine, Monitoring, Ambulatory, Cell Phone, Actigraphy, Mobile Applications
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