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eHealth4MS: Problem Detection from Wearable Activity Trackers to Support the Care of Multiple Sclerosis

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Ambient Intelligence – Software and Applications (ISAmI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1239))

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Abstract

This paper presents eHealth4MS, an assistive technology system based on wearable trackers to support the care of Multiple Sclerosis (MS). Initially, the system integrates a tracker and a smartphone to collect and unanimously store movement, sleep and heart rate (HR) data in an ontology-based knowledge base. Then, ontology patterns are used to provide an initial approach to detect problems and symptoms of interest, such as lack of movement, stress or pain, insomnia, excessive sleep, lack of sleep and restlessness. Finally, the system visualizes data trends and detected problems in dashboards and apps. This will allow patients to self-manage and for clinicians to drive effective and timely interventions and to monitor progress in future trials to evaluate the system’s accuracy and effectiveness.

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Notes

  1. 1.

    European Federation of Pharmaceutical Industries and Associations: https://www.efpia.eu.

  2. 2.

    The RADAR-BASE Platform: https://radar-base.org/.

  3. 3.

    The RADAR-CNS Project: https://www.radar-cns.org/.

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Acknowledgements

This research has been co-financed by the European Union and Greek national funds through the Operational Program Human Resources Growth, Education and Lifelong Learning, under the call for Support of Researchers with Emphasis on Young Researchers (Project: eHealth4MS).

Author information

Authors and Affiliations

  1. Information Technologies Institute, Centre for Research and Technology Hellas, 6th Km Charilaou Thermi, 57001, Thessaloniki, Greece

    Thanos G. Stavropoulos, Georgios Meditskos & Ioannis Kompatsiaris

  2. Department of Neurology III, Medical School, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece

    Sotirios Papagiannopoulos

Authors
  1. Thanos G. Stavropoulos
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  2. Georgios Meditskos
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  3. Sotirios Papagiannopoulos
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  4. Ioannis Kompatsiaris
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Corresponding author

Correspondence to Thanos G. Stavropoulos .

Editor information

Editors and Affiliations

  1. Departamento de Informática, University of Minho, ALGORITMI Center, Braga, Portugal

    Paulo Novais

  2. DIBRIS, University of Genoa, Genoa, Italy

    Gianni Vercelli

  3. Data Management Group, Technical University of Catalonia, Barcelona, Barcelona, Spain

    Josep L. Larriba-Pey

  4. Department Computer Science and Artificial Intelligence, ETS de Ingenierias Informática y de Telecomunicación, University of Granada, Granada, Spain

    Francisco Herrera

  5. BISITE Research Group, University of Salamanca, Salamanca, Salamanca, Spain

    Pablo Chamoso

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Stavropoulos, T.G., Meditskos, G., Papagiannopoulos, S., Kompatsiaris, I. (2021). eHealth4MS: Problem Detection from Wearable Activity Trackers to Support the Care of Multiple Sclerosis. In: Novais, P., Vercelli, G., Larriba-Pey, J.L., Herrera, F., Chamoso, P. (eds) Ambient Intelligence – Software and Applications . ISAmI 2020. Advances in Intelligent Systems and Computing, vol 1239. Springer, Cham. https://doi.org/10.1007/978-3-030-58356-9_1

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