In this paper, we introduce a novel health and sleep nursing assistant called “Helena” for real-time, contactless, and non-invasive monitoring that can be mounted on a bed frame to continuously monitor sleep activities (entry/exit of bed, movement, and posture changes), vital signs (heart rate and respiration rate), and falls from bed in a pervasive computing manner. The smart sensor senses bed vibrations generated by body movements to characterize sleep activities and vital signs based on advanced signal processing and machine learning methods. The device can provide information about sleep patterns, generate real-time results, and support continuous sleep assessment and health tracking. The novel method for detecting falls from bed has not been attempted before and represents a life-changing for high-risk communities, such as seniors. Comprehensive tests and validations were conducted to evaluate system performances using FDA approved and wearable devices. Our system has an accuracy of 99.5% detecting on-bed (entries), 99.73% detecting off-bed (exits), 97.92% detecting movements on the bed, 92.08% detecting posture changes, and 97% detecting falls from bed. The system estimation of heart rate (HR) ranged $\pm$2.41 beats-per-minute compared to Apple Watch Series 4, while the respiration rate (RR) ranged $\pm$0.89 respiration-per-minute compared to an FDA (Food and Drug Administration) oximeter and a metronome.

在本文中，我们介绍了一种名为“Helena”的新型健康和睡眠护理助手，用于实时、非接触和无创监测，可安装在床架上，连续监测睡眠活动（进/出床、运动和姿势变化）、生命体征（心率和呼吸率）以及以普遍计算的方式从床上摔倒。智能传感器可感应身体运动产生的床振动，以基于先进的信号处理和机器学习方法表征睡眠活动和生命体征。该设备可以提供有关睡眠模式的信息，生成实时结果，并支持持续的睡眠评估和健康跟踪。以前从未尝试过检测从床上跌倒​​的新方法，它代表了老年人等高风险社区的生活改变。使用 FDA 批准的可穿戴设备进行了全面的测试和验证，以评估系统性能。我们的系统检测床上（进入）的准确率为 99.5%，检测离床（退出）的准确率为 99.73%，检测床上的运动为 97.92%，检测姿势变化的准确率为 92.08%，检测从床上跌落的准确率为 97%。心率 (HR) 的系统估计范围$\pm$与 Apple Watch Series 4 相比，每分钟 2.41 次，而呼吸频率 (RR) 不等 $\pm$与 FDA（食品和药物管理局）血氧计和节拍器相比，每分钟呼吸 0.89 次。