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ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Computing at the Edge.

Alish Kanani, Rajat Bhattacharjya, Dip Sankar Banerjee

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 11, 2021
    PubMed
    Summary
    This summary is machine-generated.

    ApproxBioWear enhances biomedical wearable hardware efficiency using approximate computing. This technique reduces power and chip area by over 19% with minimal accuracy loss for ECG analysis.

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    Area of Science:

    • Biomedical Engineering
    • Computer Engineering
    • Hardware Acceleration

    Background:

    • Biomedical wearables are crucial for health monitoring.
    • Hardware efficiency is vital for wearable computing.
    • Approximate computing offers a method to reduce hardware resource usage.

    Purpose of the Study:

    • To investigate ApproxBioWear, a technique for efficient biomedical wearable computing at the edge.
    • To apply approximate computing to reduce hardware overhead in biomedical signal processing.

    Main Methods:

    • Implemented ApproxBioWear by approximating additions in an error-resilient biomedical signal processing algorithm.
    • Evaluated the technique on the Pan-Tompkins algorithm for QRS peak detection in ECG signals.
    • Assessed the trade-off between hardware efficiency gains and application accuracy.

    Main Results:

    • ApproxBioWear achieved an average reduction of 19.27% in power consumption.
    • The technique resulted in an average reduction of 19.71% in chip area.
    • These efficiency gains were accompanied by a marginal loss in accuracy.

    Conclusions:

    • ApproxBioWear effectively reduces power and area in biomedical wearables.
    • Approximate computing is a viable approach for efficient edge computing in healthcare.
    • The method demonstrates a favorable trade-off for ECG signal processing applications.