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Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. 16th EAI International Conference, BODYNETS 2021, Virtual Event, October 25-26, 2021, Proceedings

Research Article

Home-Based Pulmonary Rehabilitation of COPD Individuals Using the Wearable Respeck Monitor

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  • @INPROCEEDINGS{10.1007/978-3-030-95593-9_15,
        author={D. K. Arvind and T. Georgescu and C. A. Bates and D. Fischer and Q. Zhou},
        title={Home-Based Pulmonary Rehabilitation of COPD Individuals Using the Wearable Respeck Monitor},
        proceedings={Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. 16th EAI International Conference, BODYNETS 2021, Virtual Event, October 25-26, 2021, Proceedings},
        proceedings_a={BODYNETS},
        year={2022},
        month={2},
        keywords={Wearable sensors Wireless respiratory monitoring Home-based pulmonary rehabilitation Machine learning},
        doi={10.1007/978-3-030-95593-9_15}
    }
    
  • D. K. Arvind
    T. Georgescu
    C. A. Bates
    D. Fischer
    Q. Zhou
    Year: 2022
    Home-Based Pulmonary Rehabilitation of COPD Individuals Using the Wearable Respeck Monitor
    BODYNETS
    Springer
    DOI: 10.1007/978-3-030-95593-9_15
D. K. Arvind1,*, T. Georgescu1, C. A. Bates1, D. Fischer1, Q. Zhou1
  • 1: Centre for Speckled Computing, School of Informatics, University of Edinburgh
*Contact email: dka@ed.ac.uk

Abstract

Patients with Chronic Obstructive Pulmonary Disease (COPD) are advised to perform pulmonary rehabilitation exercises regularly to help manage their long-term condition. This paper describes a home-based pulmonary rehabilitation system comprising of the Respeck respiratory and physical activity monitor and a mobile App. The Respeck is a wireless sensor device worn as a plaster on the chest that monitors continuously the respiratory rate (breaths/minute) and respiratory effort/flow, and the intensity of physical activity during the rehabilitation exercises. A pulmonary rehabilitation application on the mobile device orchestrates the daily pulmonary rehabilitation exercises and harvests the respiratory and physical activity data during the exercises for onward transmission to the server for storage and analysis. This paper describes the design of an end-to-end system for guided self-management. A method is described for relating the Respeck respiratory data to the self-administered COPD Assessment Test (CAT) score reflecting the individual’s self-assessment of their condition.

Keywords
Wearable sensors Wireless respiratory monitoring Home-based pulmonary rehabilitation Machine learning
Published
2022-02-11
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-95593-9_15
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