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.

通过方便、直观地跟踪人们的健康状况，远程健康监测对于大数据驱动的精准医疗来说非常有前景。然而，当传感设备放置在离体进行远程监控时，捕获的人体信号通常非常微弱。这是因为信号从人体传播到设备时会迅速衰减。此外，如果环境中有多于一个人，则目标人的信号可能会受到干扰。针对这些关键挑战，该项目将通过创新远程信号传感和解码系统架构，推进高保真远程健康监测科学。该项目将极大地推动全民健康大数据走向普适性、高保真的、长期的大数据建设。更具体地说，该项目将设计一种新颖的深度传感器阵列解码系统，该系统利用数据驱动的深度学习算法来解码噪声和微弱信号，而不需要用于传播引起的失真估计的参考信号。此外，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感兴趣的信号。该项目将通过新课程开发、新教学实践、课程改进和广泛的学生培训进一步促进研究与教育的融合。 PI将继续扩大具有不同背景的本科生、女性和少数民族学生以及K-12学生的参与，从而有效地培养下一代工程师和研究人员。该项目将创新一种新型的深度传感器阵列解码系统，它可以从远程捕获的噪声和微弱信号中解码出感兴趣的信号，从而有望实现远程健康监测和精准医疗大数据。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，以学习噪声模式、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，从而实现智能且方便的信号解码。传感器阵列捕获的空间动态编码有关感兴趣信号的复杂信息，可以通过深度学习支持的信号解码来有效学习。此外，如果环境中有多于一个人，深度学习算法将学会分离感兴趣的信号。深度学习算法将学习并使用目标用户的特定信号模式来挖掘捕获的多传感器信号中的目标相关模式。所提出的系统架构将通过实际实验进一步评估，以证明系统的可推广创新和有效性。新颖的系统架构将为各种远程健康监测应用做出广泛贡献，通过普遍而便捷的大健康数据建立来促进国民健康，并推动用于高保真远程健康监测的深度传感器阵列解码科学。该奖项反映了 NSF 的法定使命和通过使用基金会的智力优点和更广泛的影响审查标准进行评估，该项目被认为值得支持。