TY - GEN
T1 - Mobile eHealth Platform for Home Monitoring of Bipolar Disorder
AU - Codina-Filbà, Joan
AU - Escalera, Sergio
AU - Escudero, Joan
AU - Antens, Coen
AU - Buch-Cardona, Pau
AU - Farrús, Mireia
N1 - Funding Information:
Acknowledgements. This work is part of the MYMPHA-MD project, which has been funded by the European Union under Grant Agreement N◦ 610462. It has also been partially supported by the Spanish project PID2019-105093GB-I00 (MINECO/-FEDER, UE) and CERCA Programme/Generalitat de Catalunya.), and by ICREA under the ICREA Academia programme. The last author has been funded by the Agencia Estatal de Investigación (AEI), Ministerio de Ciencia, Innovación y Universi-dades and the Fondo Social Europeo (FSE) under grant RYC-2015-17239 (AEI/FSE, UE). The authors would like to thank Ivan Latorre for his technical support and Giorgia Cistola for her help on the data preparation.
Funding Information:
This work is part of the MYMPHA-MD project, which has been funded by the European Union under Grant Agreement N◦ 610462. It has also been partially supported by the Spanish project PID2019-105093GB-I00 (MINECO/-FEDER, UE) and CERCA Programme/Generalitat de Catalunya.), and by ICREA under the ICREA Academia programme. The last author has been funded by the Agencia Estatal de Investigación (AEI), Ministerio de Ciencia, Innovación y Universi-dades and the Fondo Social Europeo (FSE) under grant RYC-2015-17239 (AEI/FSE, UE). The authors would like to thank Ivan Latorre for his technical support and Giorgia Cistola for her help on the data preparation.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Bipolar disorder
KW - Data fusion
KW - Mobile monitoring
KW - eHealth
UR - http://www.scopus.com/inward/record.url?scp=85101598355&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-67835-7_28
DO - 10.1007/978-3-030-67835-7_28
M3 - Article in proceeding
SN - 9783030678340
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 330
EP - 341
BT - MultiMedia Modeling - 27th International Conference, MMM 2021, Proceedings
A2 - Lokoc, Jakub
A2 - Skopal, Tomáš
A2 - Schoeffmann, Klaus
A2 - Mezaris, Vasileios
A2 - Li, Xirong
A2 - Vrochidis, Stefanos
A2 - Patras, Ioannis
PB - Springer
T2 - 27th International Conference on MultiMedia Modeling, MMM 2021
Y2 - 22 June 2021 through 24 June 2021
ER -