Empowering Healthcare IoT Systems with Hierarchical Edge-Based Deep Learning

Iman Azimi, Janne Takalo-Mattila, Arman Anzanpour, Amir Rahmani, Juha-Pekka Soininen, Pasi Liljeberg

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    4 Citations (Scopus)

    Abstract

    Remote health monitoring is a powerful tool to provide preventive care and early intervention for populations-at-risk. Such monitoring systems are becoming available nowadays due to recent advancements in Internet-of-Things (IoT) paradigms, enabling ubiquitous monitoring. These systems require a high level of quality in attributes such as availability and accuracy due to patients critical conditions in the monitoring. Deep learning methods are very promising in such health applications to obtain a satisfactory performance, where a considerable amount of data is available. These methods are perfectly positioned in the cloud servers in a centralized cloud-based IoT system. However, the response time and availability of these systems highly depend on the quality of Internet connection. On the other hand, smart gateway devices are unable to implement deep learning methods (such as training models) due to their limited computational capacities. In our previous work, we proposed a hierarchical computing architecture (HiCH), where both edge and cloud computing resources were efficiently exploited, allocating heavy tasks of a conventional machine learning method to the cloud servers and outsourcing the hypothesis function to the edge. Due to this local decision making, the availability of the system was highly improved. In this paper, we investigate the feasibility of deploying the Convolutional Neural Network (CNN) based classification model as an example of deep learning methods in this architecture. Therefore, the system benefits from the features of the HiCH and the CNN, ensuring a high-level availability and accuracy. We demonstrate a real-time health monitoring for a case study on ECG classifications and evaluate the performance of the system in terms of response time and accuracy.
    Original languageEnglish
    Title of host publicationProceedings - 2018 IEEE/ACM International Conference on Connected Health
    Subtitle of host publicationApplications, Systems and Engineering Technologies, CHASE 2018
    PublisherIEEE Institute of Electrical and Electronic Engineers
    Pages63-68
    Number of pages6
    ISBN (Electronic)978-1-5386-7206-8
    ISBN (Print)978-1-5386-7207-5
    Publication statusPublished - 21 Feb 2019
    MoE publication typeA4 Article in a conference publication
    EventThe Third IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2018 - Washington, United States
    Duration: 26 Sep 201828 Sep 2018
    Conference number: 3

    Conference

    ConferenceThe Third IEEE/ACM International Conference on Connected Health
    Abbreviated titleCHASE 2018
    CountryUnited States
    CityWashington
    Period26/09/1828/09/18

    Fingerprint

    Availability
    Monitoring
    Health
    Servers
    Neural networks
    Gateways (computer networks)
    Outsourcing
    Cloud computing
    Electrocardiography
    Learning systems
    Decision making
    Internet of things
    Deep learning
    Internet

    Keywords

    • Internet of Things
    • Hierarchical Computing
    • Electrocardiogram
    • Health Monitoring
    • Deep Learning
    • Convolutional neural networks (CNN)
    • Convolutional Neural Networks

    Cite this

    Azimi, I., Takalo-Mattila, J., Anzanpour, A., Rahmani, A., Soininen, J-P., & Liljeberg, P. (2019). Empowering Healthcare IoT Systems with Hierarchical Edge-Based Deep Learning. In Proceedings - 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2018 (pp. 63-68). IEEE Institute of Electrical and Electronic Engineers .
    Azimi, Iman ; Takalo-Mattila, Janne ; Anzanpour, Arman ; Rahmani, Amir ; Soininen, Juha-Pekka ; Liljeberg, Pasi. / Empowering Healthcare IoT Systems with Hierarchical Edge-Based Deep Learning. Proceedings - 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2018. IEEE Institute of Electrical and Electronic Engineers , 2019. pp. 63-68
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    abstract = "Remote health monitoring is a powerful tool to provide preventive care and early intervention for populations-at-risk. Such monitoring systems are becoming available nowadays due to recent advancements in Internet-of-Things (IoT) paradigms, enabling ubiquitous monitoring. These systems require a high level of quality in attributes such as availability and accuracy due to patients critical conditions in the monitoring. Deep learning methods are very promising in such health applications to obtain a satisfactory performance, where a considerable amount of data is available. These methods are perfectly positioned in the cloud servers in a centralized cloud-based IoT system. However, the response time and availability of these systems highly depend on the quality of Internet connection. On the other hand, smart gateway devices are unable to implement deep learning methods (such as training models) due to their limited computational capacities. In our previous work, we proposed a hierarchical computing architecture (HiCH), where both edge and cloud computing resources were efficiently exploited, allocating heavy tasks of a conventional machine learning method to the cloud servers and outsourcing the hypothesis function to the edge. Due to this local decision making, the availability of the system was highly improved. In this paper, we investigate the feasibility of deploying the Convolutional Neural Network (CNN) based classification model as an example of deep learning methods in this architecture. Therefore, the system benefits from the features of the HiCH and the CNN, ensuring a high-level availability and accuracy. We demonstrate a real-time health monitoring for a case study on ECG classifications and evaluate the performance of the system in terms of response time and accuracy.",
    keywords = "Internet of Things, Hierarchical Computing, Electrocardiogram, Health Monitoring, Deep Learning, Convolutional neural networks (CNN), Convolutional Neural Networks",
    author = "Iman Azimi and Janne Takalo-Mattila and Arman Anzanpour and Amir Rahmani and Juha-Pekka Soininen and Pasi Liljeberg",
    year = "2019",
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    Azimi, I, Takalo-Mattila, J, Anzanpour, A, Rahmani, A, Soininen, J-P & Liljeberg, P 2019, Empowering Healthcare IoT Systems with Hierarchical Edge-Based Deep Learning. in Proceedings - 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2018. IEEE Institute of Electrical and Electronic Engineers , pp. 63-68, The Third IEEE/ACM International Conference on Connected Health, Washington, United States, 26/09/18.

    Empowering Healthcare IoT Systems with Hierarchical Edge-Based Deep Learning. / Azimi, Iman; Takalo-Mattila, Janne; Anzanpour, Arman; Rahmani, Amir; Soininen, Juha-Pekka; Liljeberg, Pasi.

    Proceedings - 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2018. IEEE Institute of Electrical and Electronic Engineers , 2019. p. 63-68.

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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    AU - Takalo-Mattila, Janne

    AU - Anzanpour, Arman

    AU - Rahmani, Amir

    AU - Soininen, Juha-Pekka

    AU - Liljeberg, Pasi

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    N2 - Remote health monitoring is a powerful tool to provide preventive care and early intervention for populations-at-risk. Such monitoring systems are becoming available nowadays due to recent advancements in Internet-of-Things (IoT) paradigms, enabling ubiquitous monitoring. These systems require a high level of quality in attributes such as availability and accuracy due to patients critical conditions in the monitoring. Deep learning methods are very promising in such health applications to obtain a satisfactory performance, where a considerable amount of data is available. These methods are perfectly positioned in the cloud servers in a centralized cloud-based IoT system. However, the response time and availability of these systems highly depend on the quality of Internet connection. On the other hand, smart gateway devices are unable to implement deep learning methods (such as training models) due to their limited computational capacities. In our previous work, we proposed a hierarchical computing architecture (HiCH), where both edge and cloud computing resources were efficiently exploited, allocating heavy tasks of a conventional machine learning method to the cloud servers and outsourcing the hypothesis function to the edge. Due to this local decision making, the availability of the system was highly improved. In this paper, we investigate the feasibility of deploying the Convolutional Neural Network (CNN) based classification model as an example of deep learning methods in this architecture. Therefore, the system benefits from the features of the HiCH and the CNN, ensuring a high-level availability and accuracy. We demonstrate a real-time health monitoring for a case study on ECG classifications and evaluate the performance of the system in terms of response time and accuracy.

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    Azimi I, Takalo-Mattila J, Anzanpour A, Rahmani A, Soininen J-P, Liljeberg P. Empowering Healthcare IoT Systems with Hierarchical Edge-Based Deep Learning. In Proceedings - 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2018. IEEE Institute of Electrical and Electronic Engineers . 2019. p. 63-68