Smart Aging: Connecting Communities Using Low-Cost and Secure Sensing Technologies
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.
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Performance PeriodOctober 2020 - September 2024
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SUNY at Stony Brook
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Award Number1951880
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Lead PIFan Ye
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Co-PIShelley HORWITZ
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Co-PIPatricia Bruckenthal
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Co-PIErez Zadok
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Co-PIElinor Schoenfeld
Project Material
- SMART AGING: ENGAGING OLDER ADULTS TO GUIDE SENSOR DEVELOPMENT TO SUPPORT AGING IN PLACE
- ENGAGING OLDER ADULTS TO GUIDE LOW-COST NONWEARABLE SENSOR TECHNOLOGY DEVELOPMENT TO AGE IN PLACE: SURVEY FINDINGS
- Verifiable Sustainability in Data Centers
- HeartInsightify: Interpreting Longitudinal Heart Rate Data for Health Insights through Conformal Clustering
- Proteus: Towards a Manageability-focused Home-based Health Monitoring Infrastructure
- Improving Storage Systems Using Machine Learning
- A Study of Practical Radar-based Nighttime Respiration Monitoring at Home
- Poster: Quantifying Signal Quality Using Autoencoder for Robust RF-based Respiration Monitoring
- RF-Q: Unsupervised Signal Quality Assessment for Robust RF-based Respiration Monitoring
- Poster: Towards Robust, Extensible, and Scalable Home Sensing Data Collection
- Predicting Network Buffer Capacity for BBR Fairness
- Self-Calibrating Indoor Trajectory Tracking System Using Distributed Monostatic Radars For Large Scale Deployment
- Poster Abstract: Scaling Device-free Indoor Tracking based on Self Calibration
- Measurement Study of FMCW Radar Configurations for Non-contact Vital Signs Monitoring
- Passive and Context-Aware In-Home Vital Signs Monitoring Using Co-Located UWB-Depth Sensor Fusion
- A Machine Learning Framework to Improve Storage System Performance
- VitalHub: Robust, Non-Touch Multi-User Vital Signs Monitoring using Depth Camera-Aided UWB
- Fusing UWB and Depth Sensors for Passive and Context-Aware Vital Signs Monitoring
- Signal Quality Detection Towards Practical Non-Touch Vital Sign Monitoring
RESEARCH INTERESTS Mobile and embedded sensing systems, AI/ML algorithms and infrastructure for "Computational Screening and Surveillance (CSS)", data-centric wireless communication, edge computing, Internet-of-Things.