Sleep posture, as a crucial index for sleep quality assessment, has been widely studied in sleep analysis. In this paper, an unobtrusive smart mat system based on a dense flexible sensor array and printed electrodes along with an algorithmic framework for sleep posture recognition is proposed. With the dense flexible sensor array, the system offers a comfortable and high-resolution solution for long-term pressure sensing. Meanwhile, compared to other methods, it reduces production costs and computational complexity with a smaller area of the mat and improves portability with fewer sensors. To distinguish the sleep posture, the algorithmic framework that includes preprocessing and Deep Residual Networks (ResNet) is developed. With the ResNet, the proposed system can omit the complex hand-crafted feature extraction process and provide compelling performance. The feasibility and reliability of the proposed system were evaluated on seventeen subjects. Experimental results exhibit that the accuracy of the short-term test is up to 95.08% and the overnight sleep study is up to 86.35% for four categories (supine, prone, right, and left) classification, which outperform the most of state-of-the-art studies. With the promising results, the proposed system showed great potential in applications like sleep studies, prevention of pressure ulcers, etc.

中文翻译：

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使用不显眼的微型智能垫系统进行睡眠姿势识别的深度残差网络

睡眠姿势作为睡眠质量评估的重要指标，在睡眠分析中得到了广泛的研究。在本文中，提出了一种基于密集柔性传感器阵列和印刷电极以及睡眠姿势识别算法框架的不显眼的智能垫系统。凭借密集灵活的传感器阵列，该系统为长期压力传感提供了舒适且高分辨率的解决方案。同时，与其他方法相比，它以更小的垫子面积降低了生产成本和计算复杂度，并以更少的传感器提高了便携性。为了区分睡眠姿势，开发了包括预处理和深度残差网络（ResNet）的算法框架。通过 ResNet，所提出的系统可以省略复杂的手工特征提取过程并提供引人注目的性能。所提出的系统的可行性和可靠性在十七个科目上进行了评估。实验结果表明，对于四类（仰卧、俯卧、右侧和左侧）分类，短期测试的准确率高达95.08%，夜间睡眠研究的准确率高达86.35%，优于大多数state-of -艺术研究。凭借令人鼓舞的结果，所提出的系统在睡眠研究、预防压疮等应用中显示出巨大的潜力。4 个类别（仰卧、俯卧、右侧和左侧）分类为 35%，优于大多数最先进的研究。凭借令人鼓舞的结果，所提出的系统在睡眠研究、预防压疮等应用中显示出巨大的潜力。4 个类别（仰卧、俯卧、右侧和左侧）分类为 35%，优于大多数最先进的研究。凭借令人鼓舞的结果，所提出的系统在睡眠研究、预防压疮等应用中显示出巨大的潜力。