CCSS: Reference-free and Spatial-aware Deep Sensor Array Decoding towards High-fidelity Remote Health Monitoring

CCSS：无参考和空间感知深度传感器阵列解码，实现高保真远程健康监测

• 批准号: 2317148
• 负责人: Qingxue Zhang
• 金额: $ 24万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2317148&HistoricalAwards=false
• 关键词: CCSS; Reference; free; Spatial; aware

Remote health monitoring is highly promising for big data-driven precision medicine, through conveniently and obtrusively tracking health conditions of people. However, when the sensing device is placed off-body for remote monitoring, the captured human signal is usually very weak. This is because the signal quickly decays when it propagates from the human body to the device. Further, the signal of the target person may be interfered if there are more than one person in the environment. Targeting these crucial challenges, this project will advance the science of high-fidelity remote health monitoring, through efforts on innovating the remote signal sensing and decoding system architecture. This project will greatly advance the national health towards pervasive, high-fidelity, and long-term big data establishment. More specifically, this project will design a novel deep senor array decoding system, which leverages the data-driven deep learning algorithm to decode the noisy and weak signal, without needing a reference signal used for propagation-induced distortion estimation. Besides, the multi-sensor spatial information will be leveraged by deep learning to boost the signal fidelity and recover the signal-of-interest from noise and interferences. The project will further contribute to research-education integration through new course development, new pedagogy practices, curriculum enhancement, and broad student training. The PI will continue broadening the participation of undergraduate, women and minority students, as well as K-12 students, with diverse background, thereby effectively training the next-generation engineers and researchers.This project will innovate a novel deep sensor array decoding system, which can decode the signal-of-interest from the noisy and weak signal remotely captured, towards promising remote health monitoring and precision medicine big data. The multi-sensor signal captured by a sensor array, will be analyzed by the deep learning algorithm to learn the noise patterns, suppress the noise, and decode the high-fidelity signal. This data-driven approach does not need the reference signal that is usually used for propagation-induced distortion estimation, thereby enabling intelligent and convenient signal decoding. The spatial dynamics captured by the sensor array encode complex information about the signal-of-interest, can be effectively learned with the deep learning-empowered signal decoding. Besides, the deep learning algorithm will learn to separate the signal-of-interest if there are more than one person in the environment. The specific signal patterns for the target user will be learned and used by the deep learning algorithm to mine the target-relevant patterns in the multi-sensor signal captured. The proposed system architecture will be further evaluated with real-world experiments, to demonstrate the generalizable innovation and the effectiveness of the system. The novel system architecture will broadly contribute to various remote health monitoring applications, advance national health with pervasive and convenient big health data establishment, and promote the science on deep sensor array decoding for high-fidelity remote health monitoring.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.

远程健康监测对于大数据驱动的精密医学，通过方便而稳定地跟踪人们的健康状况，这是非常有希望的。但是，当将传感设备置于远程监视的情况下，捕获的人类信号通常非常弱。这是因为当信号从人体传播到设备时，信号很快就会衰减。此外，如果环境中有一个以上的人，目标人的信号可能会干扰。针对这些至关重要的挑战，该项目将通过创新远程信号传感和解码系统体系结构来推动高保真远程监测的科学。该项目将极大地促进全国健康朝着普遍，高保真和长期的大数据建立方向发展。更具体地说，该项目将设计一个新型的深层阵列解码系统，该系统利用数据驱动的深度学习算法来解码嘈杂和弱信号，而无需用于传播引起的失真估计的参考信号。此外，通过深度学习，将利用多传感器空间信息来提高信号保真度并从噪声和干扰中恢复利益信号。该项目将通过新的课程开发，新的教育学实践，课程增强和广泛的学生培训来进一步为研究教育融合做出贡献。 PI将继续扩大本科生，妇女和少数族裔学生以及具有不同背景的K-12学生的参与，从而有效地培训了下一代工程师和研究人员。该项目将创新一个新型的深层传感器阵列解码系统，这可以从愚蠢和弱信号的远程捕获的遥远的远程医疗数据中解释最具障碍的最具影响力的信号。传感器阵列捕获的多传感器信号将通过深度学习算法进行分析，以了解噪声模式，抑制噪声并解码高保真信号。这种数据驱动的方法不需要通常用于传播引起的失真估计的参考信号，从而实现智能和方便的信号解码。传感器阵列捕获的空间动力学编码有关利益信号的复杂信息，可以通过深度学习授权的信号解码有效地学习。此外，如果环境中有一个以上的人，那么深度学习算法将学会分开利用信号。深度学习算法将学习和使用目标用户的特定信号模式，以挖掘捕获的多传感器信号中的目标相关模式。提出的系统体系结构将通过现实世界实验进一步评估，以证明可推广的创新和系统的有效性。新型的系统体系结构将广泛地为各种远程健康监测应用程序做出贡献，通过普遍且方便的大健康数据建立来提高国家健康，并在深度传感器阵列中促进科学对高保真远程健康监控的解码。该奖项反映了NSF的法规任务，并被认为是通过基金会的知识优点和广泛的critia criter criter criter criter criter criter criter criter critia criter criter criter criter critia criter critia critia criter criter critia criter criter critia criter criter critia criteria criter critia criter criti critia均值得一提。