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A Wearable System for Stress Detection Through Physiological Data Analysis

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Ambient Assisted Living (ForItAAL 2016)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 426))

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Abstract

In the last years the impact of stress on the society has been increased, resulting in 77% of people that regularly experiences physical symptoms caused by stress with a negative impact on their personal and professional life, especially in aging working population. This paper aims to demonstrate the feasibility of detection and monitoring of stress, inducted by mental stress tests, through the analysis of physiological data collected by wearable sensors. In fact, the physiological features extracted from heart rate variability and galvanic skin response showed significant differences between stressed and not stressed people. Starting from the physiological data, the work provides also a cluster analysis based on Principal Components (PCs) able to showed a visual discrimination of stressed and relaxed groups. The developed system would support active ageing, monitoring and managing the level of stress in ageing workers and allowing them to reduce the burden of stress related to the workload on the basis of personalized interventions.

Giorgia Acerbi, Erika Rovini, Stefano Betti Equal contribution to the work.

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Acknowledgments

This work was supported by research funding provided by Trans.Safe (AmbienT Response to Avoid Negative Stress and enhance SAFEty, www.transsafe.eu) project—6th call of the Ambient Assisted Living Joint Programme (AAL JP) with the topic “ICT-based Solutions for Supporting Occupation in Life of Older Adults”

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Authors and Affiliations

  1. The BioRobotics Institute, Scuola Superiore Sant’Anna, Viale Rinaldo Piaggio, 34, 56025, Pontedera, Pisa, Italy

    Giorgia Acerbi, Erika Rovini, Stefano Betti & Filippo Cavallo

  2. Telecom Italia | TIM WHITE Joint Open Lab, Via Cardinale Pietro Maffi, 27, Pisa, Italy

    Antonio Tirri, Judit Flóra Rónai & Antonella Sirianni

  3. Life Science Institute, Scuola Superiore Sant’Anna, Piazza Martiri Della Libertà, 33, 56127, Pisa, Pisa, Italy

    Jacopo Agrimi

  4. Telecom Italia | TIM S-Cube Joint Open Lab, Via Camillo Golgi, 42, Milan, Italy

    Lorenzo Eusebi

Authors
  1. Giorgia Acerbi
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  2. Erika Rovini
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  3. Stefano Betti
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  4. Antonio Tirri
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  5. Judit Flóra Rónai
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  6. Antonella Sirianni
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  7. Jacopo Agrimi
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  8. Lorenzo Eusebi
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  9. Filippo Cavallo
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Corresponding author

Correspondence to Giorgia Acerbi .

Editor information

Editors and Affiliations

  1. Scuola Superiore Sant'Anna , Pontedera, Italy

    Filippo Cavallo

  2. DIEEI, Università degli Studi di Catania , Catania, Italy

    Vincenzo Marletta

  3. Università Politecnica delle Marche , Ancona, Italy

    Andrea Monteriù

  4. Campus Ekotecne, IMM-CNR, Lecce, Italy

    Pietro Siciliano

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Acerbi, G. et al. (2017). A Wearable System for Stress Detection Through Physiological Data Analysis. In: Cavallo, F., Marletta, V., Monteriù, A., Siciliano, P. (eds) Ambient Assisted Living. ForItAAL 2016. Lecture Notes in Electrical Engineering, vol 426. Springer, Cham. https://doi.org/10.1007/978-3-319-54283-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-54283-6_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54282-9

  • Online ISBN: 978-3-319-54283-6

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