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Biologically-Motivated Machine Learning of Natural Language and Ontology A Computational Cognitive Model
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In: http://david.wardpowers.info/Research/AI/papers/200502-MMUI-BioMotMLNLO.pdf
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| 102 |
92 Advances in Dynamic-Static Integration of Movement and Handshape Cues for Sign Language Recognition
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In: http://cvsp.cs.ntua.gr/publications/confr/TheodorakisPitsikalisMaragos_AdvancesDynamicStaticIntegrationManualCuesSignLanguageRecognition_GW2011.pdf
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| 104 |
Query clustering and IR system detection. Experiments on TREC data
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In: http://riao.free.fr/papers/49.pdf
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| 105 |
UniNE at CLEF 2012
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In: http://www.clef-initiative.eu/documents/71612/901619ab-eeeb-46ad-9188-933ed11a1641/
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| 106 |
DCU and UTA at ImageCLEFPhoto 2007
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In: http://www.clef-campaign.org/2007/working_notes/JarvelinCLEF2007.pdf
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| 107 |
Mobile eHealth platform for home monitoring of bipolar disorder
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Abstract:
Comunicació presentada al 27th International Conference on Multimedia Modeling (MMM), celebrat del 22 al 24 de juny de 2021 a Praga, República Txeca. ; 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. ; This work is part of the MYMPHA-MD project, which has been funded by the European Union under Grant Agreement Nº 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 Universidades and the Fondo Social Europeo (FSE) under grant RYC-2015-17239 (AEI/FSE, UE).
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Keyword:
Bipolar disorder; Data fusion; eHealth; Mobile monitoring
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URL: http://hdl.handle.net/10230/46367
https://doi.org/10.1007/978-3-030-67835-7_28
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