Internet of Things. User-Centric IoT. First International Summit, IoT360 2014, Rome, Italy, October 27-28, 2014, Revised Selected Papers, Part I

Research Article

Development of a Remote Monitoring System for Respiratory Analysis

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  • @INPROCEEDINGS{10.1007/978-3-319-19656-5_28,
        author={Atena Fekr and Majid Janidarmian and Katarzyna Radecka and Zeljko Zilic},
        title={Development of a Remote Monitoring System for Respiratory Analysis},
        proceedings={Internet of Things. User-Centric IoT. First International Summit, IoT360 2014, Rome, Italy, October 27-28, 2014, Revised Selected Papers, Part I},
        proceedings_a={IOT360},
        year={2015},
        month={7},
        keywords={Breath analysis Detrended fluctuation analysis Accelerometer sensor},
        doi={10.1007/978-3-319-19656-5_28}
    }
    
  • Atena Fekr
    Majid Janidarmian
    Katarzyna Radecka
    Zeljko Zilic
    Year: 2015
    Development of a Remote Monitoring System for Respiratory Analysis
    IOT360
    Springer
    DOI: 10.1007/978-3-319-19656-5_28
Atena Fekr1,*, Majid Janidarmian1,*, Katarzyna Radecka1,*, Zeljko Zilic1,*
  • 1: McGill University
*Contact email: atena.roshanfekr@mail.mcgill.ca, majid.janidarmian@mail.mcgill.ca, katarzyna.radecka@mcgill.ca, zeljko.zilic@mcgill.ca

Abstract

In order to prevent the lack of appropriate respiratory ventilation which causes brain damage and critical problems, it is required to continuously monitor the breathing signal of a patient. There are different conventional methods for capturing respiration signal, such as polysomnography and spirometer. In spite of their accuracy, these methods are expensive and could not be integrated in a body sensor network. In this work, we present a real-time cloud-based respiration monitoring platform which allows the patient to continue treatment and diagnosis from different places such as home. These remote services are designed for patients who suffer from breathing problems or sleep disorders. Our system includes calibrated accelerometer sensor, Bluetooth Low Energy (BLE) and cloud database. Based on the high correlation between spirometer and accelerometer signals, the Detrended Fluctuation Analysis (DFA) has been applied on respiration signals. The obtained results show that DFA can be used as an efficient feature while classifying the healthy people from patients suffering from breath abnormalities.