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Using a Device-Free Wi-Fi Sensing System to Assess Daily Activities and Mobility in Low-Income Older Adults: Protocol
Jane Chung1, Ingrid Pretzer-Aboff2, Pamela Parsons2
1Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA, United States.
JMIR Research Protocols
|November 12, 2024
Summary
This study explores a new, affordable Wi-Fi sensing system to monitor in-home activities and mobility for early cognitive decline detection in low-income seniors. The technology shows promise for socially vulnerable populations lacking access to traditional smart home devices.
Area of Science:
- Gerontology
- Biomedical Engineering
- Machine Learning
Background:
- Racial and ethnic minority older adults with low socioeconomic status face elevated dementia risk, yet lack resources for cognitive decline assessment.
- Existing smart home technologies (SmHT) often rely on motion sensors, failing to identify specific activities and are inaccessible to this demographic due to cost and experience barriers.
- Novel, discreet, affordable sensing technology with minimal user engagement is needed to characterize in-home activities for early detection.
Purpose of the Study:
- To assess the feasibility of a novel, device-free, low-cost Wi-Fi sensing system and machine learning (ML) algorithms.
- To enable localization and recognition of in-home activities and mobility patterns in low-income senior housing residents.
- To evaluate the system's utility for individuals with and without mild cognitive impairment.
Main Methods:
- Collaboration with a wellness care group serving low-income housing residents.
- Pilot study collecting channel state information (CSI) data from various activities (sitting, walking, meal prep) over one week.
- Videotaping activities for ground truth annotation and ML algorithm accuracy testing, supplemented by qualitative interviews on system acceptability and barriers.
Main Results:
- System deployment initiated November 2022, participant recruitment July 2023; preliminary results expected summer 2025.
- Focus on ML model feasibility for Wi-Fi sensing-based activity/mobility assessment.
- Evaluation of community-based recruitment, data collection, ground truth generation, and user acceptance of Wi-Fi sensing technology.
Conclusions:
- This feasibility study contributes to SmHT and ML for early cognitive decline detection in socially vulnerable older adults.
- The low-cost sensing device can identify at-risk individuals by tracking in-home activities and mobility, overcoming cost and information barriers.
- The technology has potential to improve functional assessment and support timely interventions for cognitive impairment.
