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Advanced Approach Using Deep Learning for Healthcare Data Analysis in IOT System

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Emergent Converging Technologies and Biomedical Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 841))

Abstract

A massive number of IOT devices creates a difficult volume of details. Cloud-based IOT computing approaches suffer from high and unpredictable network latency, leading to minimal expertise with real-time IOT deployment, such as healthcare. The data inference in edge computing starts from the data source to fix this issue (i.e., the IOT devices). Limited computational capabilities of the IOT device and power-hungry data transmission include an on-board processing-offload balance. The inference of IOT information, therefore calls for effective, lightweight techniques suited to this compromise and to confirm with limited resources in IOT devices such as wearables. A lightweight classifier performs directly on the IOT system in the first layer and determines whether to download or conduct the computer to the gateway. The second layer contains a lightweight IOT interface classifier (can identify a subset of groups only) and a complex gateway classifier (to distinguish the remaining classes). We introduced an advanced approach to utilizing deep knowledge for IOT Environmental Healthcare Data Review. The experimental findings (by utilizing a real-world data collection for the monitoring of human behaviors on a wearable IOT device) indicate greater precision (98% on average) or (90% on average).

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Authors and Affiliations

  1. Department of CSE, Glocal University, Saharanpur, U.P, India

    Shaweta Sachdeva & Aleem Ali

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  1. Shaweta Sachdeva
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  2. Aleem Ali
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Editors and Affiliations

  1. Department of Electronics and Communication Engineering, University Institute of Engineering and Technology, Kurukshetra, Haryana, India

    N. Marriwala

  2. Dean Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India

    C. C. Tripathi

  3. Department of Electronic and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Shruti Jain

  4. Co-Founder of Dew Mobility, Santa Clara University, Santa Clara, CA, USA

    Shivakumar Mathapathi

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Sachdeva, S., Ali, A. (2022). Advanced Approach Using Deep Learning for Healthcare Data Analysis in IOT System. In: Marriwala, N., Tripathi, C.C., Jain, S., Mathapathi, S. (eds) Emergent Converging Technologies and Biomedical Systems . Lecture Notes in Electrical Engineering, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-16-8774-7_14

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  • DOI: https://doi.org/10.1007/978-981-16-8774-7_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8773-0

  • Online ISBN: 978-981-16-8774-7

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