Work

Wireless Sensing-based Daily Activity Tracking System Deployment in Low-Income Senior Housing Environments

Year: 2024

Type: conference-paper

Abstract: Maintaining independence in daily activities and mobility is critical for healthy aging. Older adults who are losing the ability to care for themselves or ambulate are at a high risk of adverse health outcomes and decreased quality of life. It is essential to monitor daily activities and mobility routinely and capture early decline before a clinical symptom arises. Existing solutions use self-reports, or technology-based solutions that depend on cameras or wearables to track daily activities; however, these solutions have different issues (e.g., bias, privacy, burden to carry/recharge them) and do not fit well for seniors. In this study, we discuss a non-invasive, and low-cost wireless sensing-based solution to track the daily activities of low-income older adults. The proposed sensing solution relies on a deep learning-based fine-grained analysis of ambient WiFi signals and it is non-invasive compared to video or wearable-based existing solutions. We deployed this system in real senior housing settings for a week and evaluated its performance. Our initial results show that we can detect a variety of daily activities of the participants with this low-cost system with an accuracy of up to 76.90%.

Source: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking

DOI: 10.1145/3636534.3698115

Authors: Md Touhiduzzaman, Jane Chung, Ingrid Pretzer-Aboff, Eyuphan Bulut

Institutions: Virginia Commonwealth University, Emory University

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funder: National Institute on Aging