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Staged Inference using Conditional Deep Learning for energy efficient real-time smart diagnosis.

Maryam Parsa, Priyadarshini Panda, Shreyas Sen

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 25, 2017
    PubMed
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    This study introduces Staged Inference using Conditional Deep Learning (SICDL) for energy-efficient remote healthcare monitoring. SICDL reduces energy consumption by performing preliminary diagnoses on wearable devices and detailed analyses in the cloud, achieving 38% energy savings.

    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence in Healthcare
    • Wearable Technology

    Background:

    • Advancements in biosensors and wearables enable remote healthcare but generate large datasets.
    • Deep learning (DL) is promising for healthcare data analysis, yet conventional DL models are computationally intensive for real-time, low-power devices.

    Purpose of the Study:

    • To propose an energy-efficient deep learning approach for real-time healthcare monitoring on resource-constrained devices.
    • To develop a staged inference method that optimizes computational load for remote diagnostics.

    Main Methods:

    • Introduced Staged Inference using Conditional Deep Learning (SICDL).
    • Decomposed diagnostic tasks into preliminary (on-device) and detailed (cloud-based) stages.
    • Utilized low-complexity neural networks on wearable devices for initial screening and complex networks in the cloud for in-depth analysis.

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    Main Results:

    • Achieved a 38% energy reduction compared to conventional deep learning approaches.
    • Demonstrated feasibility using physiological sensor data from Physionet databases.
    • Enabled real-time preliminary diagnosis on low-powered on-body devices.

    Conclusions:

    • SICDL offers an energy-efficient solution for real-time healthcare monitoring systems.
    • Conditional, staged inference is effective for managing computational resources in wearable health tech.
    • The approach supports both real-time disease screening and detailed remote diagnosis.