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    IEEE Journal of Biomedical and Health Informatics
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    Ultra-wideband (UWB) radar effectively recognizes sleep postural transitions (SPTs). A novel Multi-View Learning model, SleepPoseNet (SPN), significantly outperformed deep learning methods in classifying these movements.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Machine Learning

    Background:

    • Monitoring sleep disorders requires accurate recognition of patient movements.
    • Ultra-wideband (UWB) radar applications for sleep posture classification remain underexplored.
    • Sleep postural transitions (SPTs) are key indicators of sleep quality and movement.

    Purpose of the Study:

    • To investigate the efficacy of a single-antenna UWB radar system for recognizing sleep postural transitions (SPTs).
    • To develop and evaluate a novel Multi-View Learning model, SleepPoseNet (SPN), for classifying four standard SPTs.
    • To compare the performance of SPN against existing deep learning approaches for human activity recognition using UWB data.

    Main Methods:

    • Utilized an off-the-shelf single-antenna UWB radar system to record sleep movement data from 38 volunteers.
    • Developed SleepPoseNet (SPN), a Multi-View Learning model incorporating time series data augmentation.
    • SPN was designed to capture both temporal and frequency domain features of sleeping position changes.

    Main Results:

    • SPN achieved a mean accuracy of 73.7 ±0.8% in classifying SPTs.
    • This accuracy significantly surpassed the 59.9 ±0.7% mean accuracy of a deep convolution neural network (DCNN).
    • The DCNN was applied in recent state-of-the-art UWB-based human activity recognition studies.

    Conclusions:

    • The proposed SleepPoseNet (SPN) demonstrates superior performance in recognizing sleep postural transitions using UWB radar.
    • SPN's ability to capture time and frequency features makes it effective for movement and direction classification.
    • The SPN model, enhanced with data augmentation, has potential for classifying time series data across diverse applications beyond sleep monitoring.