Smartphone-Based Recognition of States and State Changes in Bipolar Disorder Patients

Agnes Grünerbl; Amir Muaremi; Venet Osmani; Gernot Bahle; Stefan Öhler; Gerhard Tröster; Oscar Mayora; Christian Haring; Paul Lukowicz

In: IEEE Journal of Biomedical and Health Informatics (JBHI), Vol. 19, No. 1, Pages 140-148, IEEE, 2015.


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.

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence