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Link to original content: https://doi.org/10.1007/978-3-030-68007-7_5
Intelligent Advanced User Interfaces for Monitoring Mental Health Wellbeing | SpringerLink
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Intelligent Advanced User Interfaces for Monitoring Mental Health Wellbeing

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Advanced Visual Interfaces. Supporting Artificial Intelligence and Big Data Applications (AVI-BDA 2020, ITAVIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12585))

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Abstract

It has become pressing to develop objective and automatic measurements integrated in intelligent diagnostic tools for detecting and monitoring depressive states and enabling an increased precision of diagnoses and clinical decision-makings. The challenge is to exploit behavioral and physiological biomarkers and develop Artificial Intelligent (AI) models able to extract information from a complex combination of signals considered key symptoms. The proposed AI models should be able to help clinicians to rapidly formulate accurate diagnoses and suggest personalized intervention plans ranging from coaching activities (exploiting for example serious games), support networks (via chats, or social networks), and alerts to caregivers, doctors, and care control centers, reducing the considerable burden on national health care institutions in terms of medical, and social costs associated to depression cares.

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Acknowledgements

The research leading to these results has received funding from the EU H2020 under grant agreement N. 769872 (EMPATHIC) and 823907 (MENHIR), and from the Italian projects SIROBOTICS, MIUR, PNR 2015-2020, DD1735, 13/07/2017, and ANDROIDS, V:ALERE, UniCampania, D.R. 906 del 4/10/2019, prot. 157264, 17/10/2019. 7.

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Authors and Affiliations

  1. Department of Psychology and IIASS, Università della Campania “Luigi Vanvitelli”, Caserta, Italy

    Anna Esposito & Gennaro Cordasco

  2. Department Software Engineering, University of Granada, Granada, Spain

    Zoraida Callejas

  3. Research Institute for Telecommunication and Cooperation (FTK), Dortmund, Germany

    Matthias L. Hemmje

  4. Wilhelm Büchner University of Applied Science Hessen, Darmstadt, Germany

    Michael Fuchs

  5. Dipartimento di Neuroscience, Naples University “Federico”, Naples, Italy

    Mauro N. Maldonato

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  1. Anna Esposito
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  4. Michael Fuchs
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  6. Gennaro Cordasco
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Correspondence to Anna Esposito .

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  1. University of Hagen, Hagen, Germany

    Thoralf Reis

  2. University of Hagen, Hagen, Germany

    Marco X. Bornschlegl

  3. Sapienza University of Rome, Rome, Italy

    Marco Angelini

  4. University of Hagen, Hagen, Germany

    Matthias L. Hemmje

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Esposito, A., Callejas, Z., Hemmje, M.L., Fuchs, M., Maldonato, M.N., Cordasco, G. (2021). Intelligent Advanced User Interfaces for Monitoring Mental Health Wellbeing. In: Reis, T., Bornschlegl, M.X., Angelini, M., Hemmje, M.L. (eds) Advanced Visual Interfaces. Supporting Artificial Intelligence and Big Data Applications. AVI-BDA ITAVIS 2020 2020. Lecture Notes in Computer Science(), vol 12585. Springer, Cham. https://doi.org/10.1007/978-3-030-68007-7_5

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