Recognizing movements during sleep is crucial for the monitoring of patients with sleep disorders, and the utilization of ultra-wideband (UWB) radar for the classification of human sleep postures has not been explored widely. This study investigates the performance of an off-the-shelf single antenna UWB in a novel application of sleep postural transition (SPT) recognition. The proposed Multi-View Learning, entitled SleepPoseNet or SPN, with time series data augmentation aims to classify four standard SPTs. SPN exhibits an ability to capture both time and frequency features, including the movement and direction of sleeping positions. The data recorded from 38 volunteers displayed that SPN with a mean accuracy of 73.7 ±0.8 % significantly outperformed the mean accuracy of 59.9 ±0.7 % obtained from deep convolution neural network (DCNN) in recent state-of-the-art work on human activity recognition using UWB. Apart from UWB system, SPN with the data augmentation can ultimately be adopted to learn and classify time series data in various applications.

中文翻译：

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SleepPoseNet：使用 UWB 进行睡眠姿势转变识别的多视图学习


识别睡眠期间的运动对于监测睡眠障碍患者至关重要，而利用超宽带（UWB）雷达对人类睡眠姿势进行分类尚未得到广泛探索。本研究调查了现成的单天线 UWB 在睡眠姿势转换 (SPT) 识别的新颖应用中的性能。所提出的多视图学习，名为 SleepPoseNet 或 SPN，具有时间序列数据增强功能，旨在对四种标准 SPT 进行分类。 SPN 表现出捕获时间和频率特征的能力，包括睡姿的运动和方向。 38 名志愿者记录的数据显示，在人类活动的最新研究中，SPN 的平均准确度为 73.7 ±0.8%，明显优于深度卷积神经网络 (DCNN) 的平均准确度 59.9 ±0.7%。使用UWB进行识别。除了UWB系统之外，具有数据增强功能的SPN最终可以用于在各种应用中学习和分类时间序列数据。