SenSE: AI-Driven, Resilient and Adaptive Monitoring of Sleep (AI-DReAMS)

SenSE：人工智能驱动、弹性和自适应的睡眠监测 (AI-DReAMS)

• 批准号: 2037328
• 负责人: Alper Bozkurt
• 金额: $ 74.87万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2037328&HistoricalAwards=false
• 关键词: SenSE; AI; Driven; Resilient; Adaptive

A considerable amount of the population in the US and around the world suffers from a chronic sleep disorder. However, the majority of these are not diagnosed or treated. There is a vital need for new wearable technologies to increase the capacity of sleep researchers to make further advances in investigating sleep, understanding sleep pathologies, and to improve the ability of clinicians to reliably detect and treat sleep disorders. This award investigates the use of an artificial intelligence driven, reconfigurable sleep monitoring system to transform sleep research in the clinic and at-home. A sensor fusion strategy backed by artificial intelligence to ultra-miniaturize the sleep assessment instruments and explore novel sleep-related biomarker features have the transformative potential to invigorate sleep research for more efficient and accurate diagnosis and treatment of sleep disorders. Combining lower cost with better ergonomic comfort, and more efficient data analysis will pave the way for rapid translation, adoption, and effective deployment of these technologies for home-use in real-world settings. The research results from this award have the potential to positively influence the continuous monitoring instrumentation required for other chronic conditions such as heart diseases. This project enables several motivating opportunities for outreach and education including the use of technologies to interface sleep, learning new artificial intelligence and data analytics skills, analyzing sleep performance to unlock its mysteries, and the impact of sleep in the wellness and efficiency of society. Several such activities are planned with the products of this research project to reach out to younger generations, educators, other researchers in the field and public-at-large.This award integrates two parallel efforts combining innovations in hardware and data analytics: 1) enabling an adaptable and reconfigurable embedded system platform in the form factors of an adhesive patch, and 2) developing state-of-the-art machine learning techniques incorporating the data-driven models necessary for improving sleep monitoring system resilience. The hardware system fuses multimodal wearable sensors, combining Near Infrared Spectroscopy (NIRS) with other traditional sleep related signal sensors, in skin conformable substrates, to collect data on multiple body locations. The efficacy of the system will be assessed in terms of improving conformability and flexibility, and reducing the system real estate and cost. The data analytics platform includes 1) signal processing to enable data-driven metrics for signal quality assessment for a given inference task, 2) inference models based on transfer learning techniques and diverse datasets for detection of sleep events and disorders using new sensing modalities, and 3) Bayesian Neural Network supported sensor selection for improving the resilience and adaptability of sleep sensor systems. The `adaptation' will take place by selecting the most resilient sensing configurations over design iterations and in real-time during operation. In addition to allowing a novel, artificial intelligence-driven and reconfigurable tool design for sleep research, this effort will also shed light into novel multimodal biomarkers assessed noninvasively in wearable form factors for detection of sleep stages and disorders.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.

美国和世界各地有相当多的人患有慢性睡眠障碍。然而，其中大多数没有得到诊断或治疗。迫切需要新的可穿戴技术来提高睡眠研究人员的能力，以便在研究睡眠、了解睡眠病理学方面取得进一步进展，并提高临床医生可靠地检测和治疗睡眠障碍的能力。该奖项研究了人工智能驱动的可重构睡眠监测系统的使用，以改变诊所和家庭的睡眠研究。由人工智能支持的传感器融合策略可实现睡眠评估仪器的超小型化并探索新颖的睡眠相关生物标志物特征，具有激发睡眠研究的变革潜力，从而更有效、更准确地诊断和治疗睡眠障碍。将更低的成本与更好的人体工程学舒适度和更高效的数据分析相结合，将为这些技术在现实环境中的家庭使用的快速转化、采用和有效部署铺平道路。该奖项的研究结果有可能对心脏病等其他慢性疾病所需的连续监测仪器产生积极影响。该项目为推广和教育提供了多种激励机会，包括使用技术来连接睡眠、学习新的人工智能和数据分析技能、分析睡眠表现以解开其奥秘，以及睡眠对社会健康和效率的影响。计划利用该研究项目的产品开展多项此类活动，以惠及年轻一代、教育工作者、该领域的其他研究人员和广大公众。该奖项整合了硬件和数据分析创新相结合的两项并行努力：1）以粘合贴片的形式实现适应性强且可重新配置的嵌入式系统平台，2）开发最先进的机器学习技术，结合必要的数据驱动模型 提高睡眠监测系统的弹性。该硬件系统融合了多模式可穿戴传感器，将近红外光谱 (NIRS) 与其他传统睡眠相关信号传感器结合在皮肤舒适的基底中，以收集多个身体位置的数据。系统的功效将从提高一致性和灵活性以及减少系统空间和成本方面进行评估。该数据分析平台包括 1) 信号处理，以实现给定推理任务的信号质量评估的数据驱动指标；2) 基于迁移学习技术和多样化数据集的推理模型，用于使用新的传感方式检测睡眠事件和障碍；3) 贝叶斯神经网络支持传感器选择，以提高睡眠传感器系统的弹性和适应性。 “适应”将通过在设计迭代和操作过程中实时选择最具弹性的传感配置来实现。除了为睡眠研究提供一种新颖的、人工智能驱动的可重构工具设计外，这项工作还将揭示以可穿戴设备形式对新型多模式生物标志物进行非侵入性评估，以检测睡眠阶段和睡眠障碍。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Wireless Multimodal Physiological Monitoring ASIC for Injectable Implants

用于注射植入物的无线多模态生理监测 ASIC

• DOI: 10.1109/esscirc59616.2023.10268719
• 发表时间: 2023
• 期刊: Proceedings of ESSCIRC
• 作者: Zhao, Linran;Stephany, Raymond G;Han, Yiming;Ahmmed, Parvez;Bozkurt, Alper;Jia, Yaoyao
• 通讯作者: Jia, Yaoyao

Evaluation of Environmental Enclosures for Effective Ambient Ozone Sensing in Wrist-worn Health and Exposure Trackers

对腕戴式健康和暴露追踪器中有效环境臭氧传感的环境外壳进行评估

• DOI: 10.1109/sensors47087.2021.9639530
• 发表时间: 2021
• 期刊: IEEE SENSORS Conference 2021
• 作者: Latif, Tahmid;Dieffenderfer, James;Tanneeru, Akhilesh;Lee, Bongmook;Misra, Veena;Bozkurt, Alper
• 通讯作者: Bozkurt, Alper

Conformal Micropatterned Organic-Metal Electrodes for Physiological Recording

• DOI: 10.1109/sensors56945.2023.10324963
• 发表时间: 2023-10
• 期刊: 2023 IEEE SENSORS
• 作者: Kirstie M. Queener;P. Ahmmed;Mauro Victorio;Jack Twiddy;Ashley Dehn;Alec Brewer;Edgar J. Lobaton

Robust Cough Detection With Out-of-Distribution Detection

• DOI: 10.1109/jbhi.2023.3264783
• 发表时间: 2023-04
• 期刊: IEEE Journal of Biomedical and Health Informatics
• 影响因子: 7.7
• 作者: Yuhan Chen;Pankaj Attri;J. Barahona;M. Hernandez;D. Carpenter;A. Bozkurt;E. Lobaton