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A Review of Commercial and Medical-Grade Physiological Monitoring Devices for Biofeedback-Assisted Quality of Life Improvement Studies

  • Pedro Nogueira
  • Joana Urbano
  • Luís Paulo Reis
  • Henrique Lopes Cardoso
  • Daniel Castro Silva
  • Ana Paula Rocha
  • Joaquim Gonçalves
  • Brígida Mónica Faria
Mobile & Wireless Health
Part of the following topical collections:
  1. Health Information Systems & Technologies

Abstract

With the rise in wearable technology and “health culture”, we are seeing an increasing interest and affordances in studying how to not only prolong life expectancy but also in how to improve individuals’ quality of life. On the one hand, this attempts to give meaning to the increasing life expectancy, as living above a certain threshold of pain and lack of autonomy or mobility is both degrading and unfair. On the other hand, it lowers the cost of continuous care, as individuals with high quality of life indexes tend to have lower hospital readmissions or secondary complications, not to mention higher physical and mental health. In this paper, we evaluate the current state of the art in physiological therapy (biofeedback) along with the existing medical grade and consumer grade hardware for physiological research. We provide a quick primer on the most commonly monitored physiologic metrics, as well as a brief discussion on the current state of the art in biofeedback-assisted medical applications. We then go on to present a comparative analysis between medical and consumer grade biofeedback devices and discuss the hardware specifications and potential practical applications of each consumer grade device in terms of functionality and adaptability for controlled (laboratory) and uncontrolled (field) studies. We end this article with some empirical observations based on our study so that readers might use take them into consideration when arranging a laboratory or real-world experience, thus avoiding costly time delays and material expenditures.

Keywords

Psychophysiology Quality of life Biofeedback Consumer grade hardware Fitness tracking 

Notes

Acknowledgements

This article is a result of the project QVida+: Estimação Contínua de Qualidade de Vida para Auxílio Eficaz à Decisão Clínica, NORTE-01-0247-FEDER-003446, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). The authors also acknowledge to the strategic project LIACC (PEst-UID/CEC/00027/2013).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Pedro Nogueira
    • 1
    • 2
  • Joana Urbano
    • 1
    • 2
  • Luís Paulo Reis
    • 1
    • 3
  • Henrique Lopes Cardoso
    • 1
    • 2
  • Daniel Castro Silva
    • 1
    • 2
  • Ana Paula Rocha
    • 1
    • 2
  • Joaquim Gonçalves
    • 4
  • Brígida Mónica Faria
    • 1
    • 5
  1. 1.Artificial Intelligence and Computer Science Laboratory (LIACC)PortoPortugal
  2. 2.Faculty of Engineering of the University of Porto (FEUP)PortoPortugal
  3. 3.School of Engineering of the University of Minho (EEUM)BragaPortugal
  4. 4.EST/IPCA – Technology School/Polytechnic Institute of Cávado and AveBarcelosPortugal
  5. 5.ESS/P.Porto – Higher School of Health/Porto PolytechnicPortoPortugal

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