Collaborative Research: PPoSS: LARGE: Principles and Infrastructure of Extreme Scale Edge Learning for Computational Screening and Surveillance for Health Care

合作研究：PPoSS：大型：用于医疗保健计算筛查和监视的超大规模边缘学习的原理和基础设施

• 批准号: 2119299
• 负责人: Fan Ye
• 金额: $ 212.72万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2119299&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; LARGE; Principles

This project investigates a completely new cross-disciplinary concept of “Computational Screening and Surveillance (CSS)” that utilizes edge learning to detect early indicators of diseases, and monitor health changes in both individuals and populations. CSS analyzes and interprets continuous and heterogeneous physical and physiologic sensing-data streams of human subjects to produce real-time information, knowledge, and insights about their health status. The project’s novelty is a data-driven paradigm that revolutionizes the understanding, prediction, intervention, treatment, and management of acute/infectious, chronic physical and psychological diseases. The project’s impacts are enormous social and economic benefits to individuals, organizations, and the healthcare system: early detection, preemptive intervention and management can lead to greatly improved quality of care, and huge savings for multiple diseases each costing hundreds of billions of dollars every year.The investigators design, develop and evaluate principles and solutions for CSS enabled by extreme-scale edge learning spanning four dimensions: data modalities, health conditions and data patterns, Artificial Intelligence/Machine Learning (AI/ML) algorithms and models, and individuals/populations. The design is guided by four principles: exploit scale and heterogeneity, design for uncertainty, privacy as a first-class citizen, and faults and attacks as a norm. The investigators will 1) design AI/ML algorithms for learning data patterns and correlations for diverse health conditions in both individuals and populations at extreme scales; 2) quantify theoretical bounds on the tradeoffs between security, privacy protection, and learning accuracy in order to protect against various attacks on data and models at both the edge and cloud; 3) develop programming abstractions for automated exploration of competing AI/ML methods under uncertainty, and system mechanisms to protect stream processing integrity against sensitive data disclosure and faulty/malicious analytics; and 4) devise neural architectures and accelerators for computation efficiency at the constrained edge, data efficiency using limited training sets, and human efficiency utilizing AutoML.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.

该项目研究了一种全新的跨学科概念“计算筛查和监测”，它利用边缘学习来检测疾病的早期指标，并监测个人和人群的健康变化。CS分析和解释人类受试者的连续和不同种类的生理和生理传感数据流，以产生关于他们健康状况的实时信息、知识和洞察。该项目的新颖性是一种数据驱动的范式，它彻底改变了对急性/传染性、慢性身体和心理疾病的理解、预测、干预、治疗和管理。该项目的影响为个人、组织和医疗保健系统带来了巨大的社会和经济利益：早期发现、先发制人的干预和管理可以极大地提高医疗质量，并为多种疾病节省巨额成本，每种疾病每年花费数千亿美元。研究人员设计、开发和评估由极大规模边缘学习支持的CS的原则和解决方案，涉及四个维度：数据模式、健康状况和数据模式、人工智能/机器学习(AI/ML)算法和模型以及个人/人口。设计遵循四个原则：利用规模和异质性，针对不确定性进行设计，将隐私作为一等公民，将错误和攻击作为规范。调查人员将1)设计AI/ML算法，用于在极端规模上学习数据模式以及个人和群体中不同健康状况的相关性；2)量化安全性、隐私保护和学习准确性之间的理论界限，以防止对边缘和云中的数据和模型的各种攻击；3)开发编程抽象，用于在不确定情况下自动探索竞争的AI/ML方法，以及保护流处理完整性免受敏感数据泄露和错误/恶意分析的系统机制；以及4)设计神经架构和加速器，以提高受约束边缘的计算效率，使用有限的训练集提高数据效率，并利用AutoML提高人类效率。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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

Demo: Fusing UWB and Depth Sensors for Passive and Context-Aware Vital Signs Monitoring

演示：融合 UWB 和深度传感器进行被动和上下文感知生命体征监测

• 发表时间: 2022
• 期刊: and Engineering Technologies (CHASE 2021
• 作者: Xie, Zongxing;Zhou, Bing;Cheng, Xi;Schoenfeld, Elinor;Ye, Fan
• 通讯作者: Ye, Fan

Poster: Towards Robust, Extensible, and Scalable Home Sensing Data Collection

海报：实现稳健、可扩展和可扩展的家庭传感数据收集

• 发表时间: 2023
• 期刊: Systems and Engineering Technologies
• 作者: Elbadry, Mohammed;Liu, Mengjing;Hua, Yindong;Xie, Zongxing;Ye, Fan
• 通讯作者: Ye, Fan

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

RF-Q: Unsupervised Signal Quality Assessment for Robust RF-based Respiration Monitoring

• DOI: 10.1145/3580252.3586988
• 发表时间: 2023-06
• 期刊: 2023 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)
• 作者: Zongxing Xie;Ava Nederlander;Isac Park;Fan Ye

Poster: Quantifying Signal Quality Using Autoencoder for Robust RF-based Respiration Monitoring

海报：使用自动编码器量化信号质量以实现稳健的基于射频的呼吸监测

• 发表时间: 2023
• 期刊: Systems and Engineering Technologies
• 作者: Xie, Zongxing;Nederlander, Ava;Park, Isac;Ye, Fan
• 通讯作者: Ye, Fan