Mobile eHealth platform for home monitoring of bipolar disorder

Codina Filbà, Joan; Escalera, Sergio; Escudero, Joan; Antens, Coen; Buch-Cardona, Pau; Farrús, Mireia

doi:http://dx.doi.org/10.1007/978-3-030-67835-7_28

Mobile eHealth platform for home monitoring of bipolar disorder

Citation

Codina-Filbà J, Escalera S, Escudero J, Antens C, Buch-Cardona P, Farrús M. Mobile eHealth platform for home monitoring of bipolar disorder. In: Lokoč J, Skopal T, Schoeffmann K, Mezaris V, Li X, Vrochidis S, Patras I, editors. Proceedings of the 27th International Conference on Multimedia Modeling (MMM); 2021 Jun 22-24; Prague, Czech Republic. Berlin: Springer; 2021. p. 330-41. (LNCS; no. 12572). DOI: 10.1007/978-3-030-67835-7_28

Abstract

People suffering Bipolar Disorder (BD) experiment changes in mood status having depressive or manic episodes with normal periods in the middle. BD is a chronic disease with a high level of non-adherence to medication that needs a continuous monitoring of patients to detect when they relapse in an episode, so that physicians can take care of them. Here we present MoodRecord, an easy-to-use, non-intrusive, multilingual, robust and scalable platform suitable for home monitoring patients with BD, that allows physicians and relatives to track the patient state and get alarms when abnormalities occur. MoodRecord takes advantage of the capabilities of smartphones as a communication and recording device to do a continuous monitoring of patients. It automatically records user activity, and asks the user to answer some questions or to record himself in video, according to a predefined plan designed by physicians. The video is analysed, recognising the mood status from images and bipolar assessment scores are extracted from speech parameters. The data obtained from the different sources are merged periodically to observe if a relapse may start and if so, raise the corresponding alarm. The application got a positive evaluation in a pilot with users from three different countries. During the pilot, the predictions of the voice and image modules showed a coherent correlation with the diagnosis performed by clinicians.