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

远程健康监测是大数据驱动的精准医疗，通过方便和显著的跟踪人们的健康状况，是非常有前景的。然而，当传感装置离体进行远程监测时，捕获的人体信号通常非常微弱。这是因为当信号从人体传播到设备时，它会迅速衰减。此外，如果环境中有一个以上的人，则目标人的信号可能受到干扰。针对这些关键挑战，该项目将通过努力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向普适、高保真、长期的大数据建设迈进。更具体地说，该项目将设计一种新型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、新教学法实践、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将继续扩大不同背景的大学生、女性、少数民族学生以及K-12学生的参与，从而有效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，该系统可以从远程捕获的噪声和微弱信号中解码感兴趣的信号，为远程健康监测和精准医疗大数据提供前景。由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态编码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有效地学习。此外，如果环境中有不止一个人，深度学习算法将学习分离感兴趣信号。目标用户的特定信号模式将被深度学习算法学习和使用，以挖掘捕获的多传感器信号中与目标相关的模式。我们将通过实际实验进一步评估所提出的系统架构，以证明系统的可推广创新和有效性。该新型系统架构将广泛应用于各种远程健康监测应用，以普适、便捷的健康大数据建设推进国民健康，推动高保真远程健康监测的深度传感器阵列解码技术的发展。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。

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.