SCC-IRG Track 1: Smart Aging: Connecting Communities Using Low-Cost and Secure Sensing Technologies

SCC-IRG 第 1 轨道：智能老龄化：使用低成本和安全的传感技术连接社区

• 批准号: 1951880
• 负责人: Fan Ye
• 金额: $ 170.01万
• 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 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1951880&HistoricalAwards=false
• 关键词: SCC; IRG; Track; Smart; Aging

The rapid growth of our aging population, nicknamed the "silver tsunami", causes both social and economic challenges. Without the appropriate tools to support independence, individuals age 65+ will overwhelm the limited resources of families, providers, and other supporting stakeholders nationwide. This project will engage diverse stakeholder groups to develop holistic technological and social solutions to address these challenges and promote the welfare, quality of life, autonomy, and dignity of older adults aging at home. The project develops technologies that can detect emergency, emergent, transitional and long-term changes in individuals’ physical, social and cognitive states at times where prevention/early intervention can have the greatest benefits. These technologies will be paired novel social approaches that foster technology adoption by diverse populations. Overall, the approach will bring effective, cost-saving solutions at scale to many communities. The team will train students for interdisciplinary careers, cultivate the next generation of technology-savvy healthcare workers, and transfer the project findings to communities across the nation.This project will: (1) engage older adults, family and non-family caregivers, supporting institutions, and professionals within Suffolk County, Long Island, New York to identify health and social challenges that aging adults face; (2) design, develop, pilot test, and evaluate robust, secure, and affordable continuous health data collection and analysis solutions to automate health change detection, classification, and prediction (e.g., disease onset/progression/resolution); (3) develop social solutions and best practices that foster greater adoption of sensing technologies, share data effectively with stakeholders, and measure social determinants of health using a quantitative, data-driven approach; and (4) co-develop and co-evaluate the technical and social solutions with diverse community stakeholders---and educate/train students, residents, and providers end-to-end for technology assisted aging in place. The results will include novel technologies targeting challenges of older adults and best social practices in technology adoption to maximize the benefits for all stakeholders.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.

人口老龄化的迅速增长，被称为“银海啸”，给社会和经济带来了挑战。如果没有适当的工具来支持独立，65岁以上的个人将压倒全国范围内家庭，供应商和其他支持利益攸关方的有限资源。该项目将吸引不同的利益相关者群体，以开发整体的技术和社会解决方案，以应对这些挑战，并促进在家养老的老年人的福利，生活质量，自主权和尊严。该项目开发的技术可以在预防/早期干预可以产生最大效益的时候，检测个人身体、社会和认知状态的紧急、紧急、过渡和长期变化。这些技术将与新的社会方法相结合，促进不同人群对技术的采用。总体而言，该方法将为许多社区带来有效、节省成本的大规模解决方案。该项目将培养学生跨学科的职业生涯，培养下一代精通技术的医疗保健工作者，并将项目成果转移到全国各地的社区。该项目将：（1）让纽约长岛萨福克县的老年人，家庭和非家庭护理人员，支持机构和专业人士参与，以确定老年人面临的健康和社会挑战;（2）设计、开发、试点测试和评估稳健、安全和负担得起的连续健康数据收集和分析解决方案，以自动化健康变化检测、分类和预测（例如，（3）制定社会解决方案和最佳做法，促进更多地采用传感技术，与利益攸关方有效分享数据，并使用定量、数据驱动的方法衡量健康的社会决定因素;和（4）与不同的社区利益相关者共同开发和共同评估技术和社会解决方案-并教育/培训学生，居民，和提供商端到端的技术辅助老龄化到位。该奖项反映了NSF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

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

Predicting Network Buffer Capacity for BBR Fairness

• 发表时间: 2022
• 作者: I. Akgun;Santiago Vargas;Michael Arkhangelskiy;Andrew Burford;Michael McNeill;A. Balasubramanian;Anshul Gandhi;E. Zadok

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

Improving Storage Systems Using Machine Learning

• DOI: 10.1145/3568429
• 发表时间: 2022-11
• 期刊: ACM Transactions on Storage
• 影响因子: 1.7
• 作者: I. Akgun;A. S. Aydin;Andrew Burford;Michael McNeill;Michael Arkhangelskiy;E. Zadok