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Smart Assisted Living pp 43–62Cite as

Unobtrusive Sensing Solution for Post-stroke Rehabilitation

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Part of the book series: Computer Communications and Networks ((CCN))

Abstract

This Chapter proposes an unobtrusive sensing solution for monitoring post-stroke rehabilitation exercises within a home environment. It begins with the definition of stroke, its types, statistics and effects. An overview of stroke rehabilitation techniques ranging from multiple exercising and isolated approaches to motor skill learning, mirror imagery, adjuvant therapies and technology-based interventions are all presented in this Chapter. In addition, the potential for the use of unobtrusive sensing solutions such as thermal, radar, optical and ultrasound sensing are considered with practical examples. The Seebeck, time of flight (ToF) and Doppler principles, which are associated with a number of the sensing solutions, are also explained. Furthermore, sensor data fusion (SDF) and its architectures such as centralized, distributed and hybrid architectures are explained. A few examples of SDF applications in automobile and terrestrial light detection are included in addition to the advantages and disadvantages of the approaches. Unobtrusive sensing solutions and their applications in healthcare are captured in this Chapter. The Chapter includes details of initial experimental results on post-stroke rehabilitation exercises which were obtained using thermal and radar sensing solutions. The Chapter concludes with an outline of recommendations for future research.

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Acknowledgements

This project is supported by the European Union’s INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB).

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Authors and Affiliations

  1. School of Computing, Ulster University, Newtownabbey, BT37 0QB, Northern Ireland, UK

    Idongesit Ekerete & Chris Nugent

  2. Dundalk Institute of Technology, NetwellCASALA, Dundalk, Republic of Ireland

    Oonagh M. Giggins

  3. NIBEC, Ulster University, Newtownabbey, BT37 0QB, Northern Ireland, UK

    James McLaughlin

Authors
  1. Idongesit Ekerete
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  2. Chris Nugent
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  3. Oonagh M. Giggins
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  4. James McLaughlin
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Corresponding author

Correspondence to Idongesit Ekerete .

Editor information

Editors and Affiliations

  1. School of Computer Science & Informatics, De Montfort University, Leicester, UK

    Feng Chen

  2. Life Supporting Technologies, Universidad Politecnica de Madrid, Madrid, Spain

    Rebeca I. García-Betances

  3. School of Computing, Ulster University, Belfast, UK

    Liming Chen

  4. Life Supporting Technologies, Universidad Politecnica de Madrid, Madrid, Spain

    María Fernanda Cabrera-Umpiérrez

  5. School of Computing, Ulster University, Newtownabbey, UK

    Chris Nugent

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Ekerete, I., Nugent, C., Giggins, O.M., McLaughlin, J. (2020). Unobtrusive Sensing Solution for Post-stroke Rehabilitation. In: Chen, F., García-Betances, R., Chen, L., Cabrera-Umpiérrez, M., Nugent, C. (eds) Smart Assisted Living. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-25590-9_3

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  • DOI: https://doi.org/10.1007/978-3-030-25590-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-25589-3

  • Online ISBN: 978-3-030-25590-9

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