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A Public Dataset of 24-h Multi-Levels Psycho-Physiological Responses in Young Healthy Adults

Author

Listed:
  • Alessio Rossi

    (Department of Computer Science, University of Pisa, 56127 Pisa, Italy)

  • Eleonora Da Pozzo

    (Department of Pharmacy, University of Pisa, 56126 Pisa, Italy)

  • Dario Menicagli

    (MOMILab, IMT School for Advanced Studies Lucca, 55100 Lucca, Italy)

  • Chiara Tremolanti

    (Department of Pharmacy, University of Pisa, 56126 Pisa, Italy)

  • Corrado Priami

    (Department of Computer Science, University of Pisa, 56127 Pisa, Italy
    COSBI, 38068 Rovereto, Italy)

  • Alina Sîrbu

    (Department of Computer Science, University of Pisa, 56127 Pisa, Italy)

  • David A. Clifton

    (Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK)

  • Claudia Martini

    (Department of Pharmacy, University of Pisa, 56126 Pisa, Italy)

  • Davide Morelli

    (Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
    Huma Therapeutics Limited, London SW1P 4QP, UK)

Abstract

Wearable devices now make it possible to record large quantities of physiological data, which can be used to obtain a clearer view of a person’s health status and behavior. However, to the best of our knowledge, there are no open datasets in the literature that provide psycho-physiological data. The Multilevel Monitoring of Activity and Sleep in Healthy people (MMASH) dataset presented in this paper provides 24 h of continuous psycho-physiological data, that is, inter-beat intervals data, heart rate data, wrist accelerometry data, sleep quality index, physical activity (i.e., number of steps per second), psychological characteristics (e.g., anxiety status, stressful events, and emotion declaration), and sleep hormone levels for 22 participants. The MMASH dataset will enable the investigation of possible relationships between the physical and psychological characteristics of people in daily life. Data were validated through different analyses that showed their compatibility with the literature.

Suggested Citation

  • Alessio Rossi & Eleonora Da Pozzo & Dario Menicagli & Chiara Tremolanti & Corrado Priami & Alina Sîrbu & David A. Clifton & Claudia Martini & Davide Morelli, 2020. "A Public Dataset of 24-h Multi-Levels Psycho-Physiological Responses in Young Healthy Adults," Data, MDPI, vol. 5(4), pages 1-15, September.
    • Handle: RePEc:gam:jdataj:v:5:y:2020:i:4:p:91-:d:419814
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    Cited by:

    1. Olga Vl. Bitkina & Jaehyun Park & Jungyoon Kim, 2022. "Modeling Sleep Quality Depending on Objective Actigraphic Indicators Based on Machine Learning Methods," IJERPH, MDPI, vol. 19(16), pages 1-14, August.

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