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Skeleton-Based Abnormal Behavior Detection Using Secure Partitioned Convolutional Neural Network Model.

Jiefan Qiu, Xinlei Yan, Wei Wang

    IEEE Journal of Biomedical and Health Informatics
    |December 23, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a secure partitioned Convolutional Neural Network (SP-CNN) for detecting abnormal behavior in patients with cognitive impairment. The SP-CNN enhances privacy and efficiency by processing data locally on IoT devices, improving real-time health monitoring.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Internet of Things (IoT)

    Background:

    • Abnormal behavior detection is crucial for cognitive impairment patient care.
    • Convolutional Neural Networks (CNNs) show high accuracy but face privacy and computational challenges on edge devices.
    • Existing methods struggle with real-time performance and data security during transmission.

    Purpose of the Study:

    • To develop a secure and efficient abnormal behavior detection system for cognitive impairment.
    • To address privacy concerns associated with cloud-based CNN models.
    • To improve real-time performance of abnormal behavior detection on IoT devices.

    Main Methods:

    • Proposed a secure partitioned CNN (SP-CNN) model for skeleton-based abnormal behavior detection.
    • Implemented collaborative computing by distributing CNN layers between cloud and IoT devices.
    • Designed a Channel State Information (CSI) based encryption method for data security.

    Main Results:

    • SP-CNN achieved at least 33.2% higher efficiency compared to state-of-the-art methods.
    • Demonstrated a detection accuracy of 97.54% for abnormal behavior.
    • Reduced privacy disclosure risks by processing data locally on IoT devices.

    Conclusions:

    • SP-CNN offers a secure and efficient solution for abnormal behavior detection in cognitive impairment.
    • The partitioned model effectively balances privacy, security, and real-time performance.
    • This approach enhances remote health monitoring capabilities for vulnerable populations.