Collaborative Research: PPoSS: Planning: Principles for Edge Sensing and Computing for Personalized, Precision Healthcare at National Scale

合作研究：PPoSS：规划：全国范围内个性化精准医疗的边缘传感和计算原则

• 批准号: 2028952
• 负责人: Fan Ye
• 金额: $ 14.92万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028952&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Planning; Principles

Personalized, precision healthcare (PPH) utilizing edge sensing-computing can collect, analyze and interpret continuous, multi-modality data, both physical and physiologic, producing information, knowledge and insight needed for real-time disease onset and progression monitoring at both the individual and population levels. This planning proposal will (i) identify the challenges and investigate the principles and potential solutions for the edge sensing-computing paradigm; (ii) engage diverse academic, community and government stakeholders to collectively define the functional and performance requirements for PPH; and (iii) create and validate preliminary approaches and devise a concrete, detailed plan for scaling PPH to national levels. It is well aligned with NSF’s mission to “advance the national health, prosperity and welfare.” This project can generate enormous social and economic benefits for communities, healthcare systems, and other stakeholders. If successful, the project will enable the monitoring of epidemics (e.g. disease outbreaks/spread, early detection/preemptive intervention of acute/infectious diseases) and the management of chronic physical and psychological conditions. The PIs will 1) disseminate publications, data and systems in academic, industry and community venues; 2) integrate CISE student education (including female and under-represented minorities) at different levels; 3) mentor high-school students on joint health-technology research; 4) cultivate a technology-literate healthcare workforce; and 5) pilot the technologies for immediate benefits to nearby communities while studying how to scale to other rural, suburban, and city settings.This project will explore, design, and evaluate potential solutions for enhancing the scalability of edge sensing-computing-based PPH in four dimensions of different types of sensing data, analytic algorithms, diseases, health conditions, and population sizes. The PIs will identify challenges and validate approaches guided by three principles: privacy as a first-class citizen, design for faults and exploitation of scale. The team will: 1) define new abstractions and quantifiable metrics for end-to-end security and privacy guarantees across hardware, software and application stack; 2) investigate systems for multi-temporal resolution processing of heterogeneous healthcare data, incorporating composition of components from possibly untrusted third parties and accommodate noises, disturbances or even adversary-controlled data; 3) explore novel AI/machine-learning algorithms suitable for PPH learning and inference, including AutoML for neural-network architecture search, model compression and federated learning at extreme scale while meeting security, privacy and robustness constraints; and 4) develop heterogeneous hardware accelerators and general design methodologies and tools for neural-hardware architecture co-design, efficient acceleration for time-series, point-cloud and language/sound understanding, and on-device edge training.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.

利用边缘传感计算的个性化、精准医疗（PPH）可以收集、分析和解释连续的、多模态的物理和生理数据，产生信息、知识和洞察力，以便在个人和群体层面实时监测疾病的发生和进展。该规划提案将(i)确定挑战，并研究边缘感知计算范式的原则和潜在解决方案；（ii）让不同的学术、社区和政府利益相关者共同确定PPH的功能和性能要求；（iii）创建和验证初步方法，并制定具体、详细的计划，将PPH推广到国家层面。这与美国国家科学基金会“促进国家健康、繁荣和福利”的使命完全一致。该项目可以为社区、医疗保健系统和其他利益相关者带来巨大的社会和经济效益。如果成功，该项目将能够监测流行病（例如疾病的爆发/传播、急性/传染病的早期发现/先发制人的干预）和管理慢性身体和心理状况。pi将1)在学术、工业和社区场所传播出版物、数据和系统；2)整合不同层次的CISE学生教育（包括女性和少数族裔）；3)指导高中生进行联合卫生技术研究；4)培养一支具有技术素养的医疗保健队伍；5)在研究如何推广到其他农村、郊区和城市环境的同时，对这些技术进行试点，为附近社区带来直接利益。该项目将探索、设计和评估潜在的解决方案，以在不同类型的传感数据、分析算法、疾病、健康状况和人口规模的四个维度上增强基于边缘传感计算的PPH的可扩展性。pi将识别挑战并验证由三个原则指导的方法：作为一流公民的隐私、针对缺陷的设计和规模利用。该团队将：1)为跨硬件、软件和应用程序栈的端到端安全和隐私保证定义新的抽象和可量化指标；2)研究异构医疗数据的多时间分辨率处理系统，包括来自可能不受信任的第三方的组成部分，并适应噪声、干扰甚至对手控制的数据；3)探索适合PPH学习和推理的新型AI/机器学习算法，包括AutoML用于神经网络架构搜索、模型压缩和极端规模的联邦学习，同时满足安全性、隐私性和鲁棒性约束；4)开发异构硬件加速器和通用设计方法和工具，用于神经硬件架构协同设计，时间序列、点云和语言/声音理解的有效加速，以及设备上的边缘训练。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。

项目成果

DeepVS: a deep learning approach for RF-based vital signs sensing

• DOI: 10.1145/3535508.3545554
• 发表时间: 2022-08
• 期刊: Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics
• 作者: Zongxing Xie;Hanrui Wang;Song Han;E. Schoenfeld;Fan Ye

The Role of Unobtrusive Home-Based Continuous Sensing in the Management of Postacute Sequelae of SARS CoV-2.

• DOI: 10.2196/32713
• 发表时间: 2022-01-26
• 期刊: Journal of medical Internet research
• 影响因子: 7.4
• 作者: Corman BHP;Rajupet S;Ye F;Schoenfeld ER
• 通讯作者: Schoenfeld ER

Passive and Context-Aware In-Home Vital Signs Monitoring Using Co-Located UWB-Depth Sensor Fusion

• DOI: 10.1145/3549941
• 发表时间: 2022-07
• 期刊: ACM Transactions on Computing for Healthcare
• 作者: Zongxing Xie;Bing Zhou;Xi Cheng;E. Schoenfeld;Fan Ye

VitalHub: Robust, Non-Touch Multi-User Vital Signs Monitoring using Depth Camera-Aided UWB

• DOI: 10.1109/ichi52183.2021.00056
• 发表时间: 2021-08
• 期刊: 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)
• 作者: Zongxing Xie;Bing Zhou;Xi Cheng;E. Schoenfeld;Fan Ye

Signal quality detection towards practical non-touch vital sign monitoring

• DOI: 10.1145/3459930.3469526
• 发表时间: 2021-08
• 期刊: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
• 作者: Zongxing Xie;Bing Zhou;Fan Ye