STTR Phase I: BedDot: A Contactless Sensor Device for Sleep Activity Monitoring

这项小企业技术转移（STTR）第一阶段项目的更广泛影响包括智能传感技术的进步，以及通过提供睡眠期间的实时安全、活动和健康监测，改善老年人的生活质量和护理；并向他们的亲人和护理人员发送警报、报告和分析。这部分人口的增长、家庭规模的缩小和流动性的增加给老年护理带来了重大挑战。根据美国人口普查局的预测，到2022年，65岁及以上的美国人将增加到5500万，到2030年将增加到7000万；在这个群体中，85岁以上的人口是增长最快的部分。老年人和护理人员将受益于该项目开发的新型传感器技术，无论他们是住在自己家中还是住在辅助生活设施中，都有助于提高医疗保健质量并降低成本。该项目开发的先进信号处理和机器学习技术将推动数据分析和智能传感领域的发展。该项目是第一个开发基于振动传感的实时非接触式睡眠监测设备的项目。该传感器将在各种建筑环境的床垫下提供可靠的睡眠活动和生命体征监测。该项目将标志着开发非接触式血压监测功能的首次尝试。先进的信号处理和机器学习算法将在生命体征估计（心率、呼吸频率和血压）和睡眠活动识别（床上/下床、运动、姿势变化）方面得到改进和验证。数据分析算法开发的一个关键挑战是自适应物理和噪声环境的变化。各种算法和功能将集成到一个设备与一个友好的图形界面；然后在不同的相关环境中系统地评估产品的优势和局限性，并与其他设备进行比较。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impacts of this Small Business Technology Transfer (STTR) Phase I project include the technology advancement of smart sensing and the improvement of the quality of life and care of seniors, by providing real-time safety, activity and health monitoring during sleep; and sending alerts, reports and analysis to their loved ones and caregivers. The growth of this demographic segment, the reduction of family size, and increased mobility bring significant challenges to senior care. According to U.S. Census Bureau projections, the number of Americans 65 and older will increase to 55 million in 2022, and to 70 million by 2030; of this group, the population over 85 years of age is the fastest growing segment. Seniors and caregivers will benefit from the new sensor technology developed in this project, whether they live in their own homes or in assisted-living facilities, contributing to healthcare quality improvement and cost reduction. The advanced signal processing and machine learning techniques developed in this project will advance the field of data analytics and smart sensing. The proposed project is the first to develop a real-time contactless sleep monitoring device based on vibration sensing. The sensor will provide reliable monitoring of sleep activities and vital signs while placed under mattresses in various building environments. This project will mark the first attempt to develop a contactless blood pressure monitoring function. The advanced signal processing and machine learning algorithms will be refined and validated regarding vital sign estimation (heart rate, respiration rate, and blood pressure) and sleep activity recognition (entry/exit of the bed, movement, posture change). A key challenge of data analytics algorithm development is to self-adapt to changes in the physical and noise environment. Various algorithms and functions will be integrated into one device with a user-friendly graphic interface; then the product's advantages and limitations will be evaluated systematically in different relevant environments and compared with other devices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.