Background: Older adults belonging to racial or ethnic minorities with low socioeconomic status are at an elevated risk ofdeveloping dementia, but resources for assessing functional decline and detecting cognitive impairment are limited. Cognitiveimpairment affects the ability to perform daily activities and mobility behaviors. Traditional assessment methods have drawbacks,so smart home technologies (SmHT) have emerged to offer objective, high-frequency, and remote monitoring. However, thesetechnologies usually rely on motion sensors that cannot identify specific activity types. This group often lacks access to thesetechnologies due to limited resources and technology experience. There is a need to develop new sensing technology that isdiscreet, affordable, and requires minimal user engagement to characterize and quantify various in-home activities. Furthermore,it is essential to explore the feasibility of developing machine learning (ML) algorithms for SmHT through collaborations betweenclinical researchers and engineers and involving minority, low-income older adults for novel sensor development. Objective: This study aims to examine the feasibility of developing a novel channel state information-based device-free,low-cost Wi-Fi sensing system, and associated ML algorithms for localizing and recognizing different patterns of in-homeactivities and mobility in residents of low-income senior housing with and without mild cognitive impairment. Methods: This feasibility study was conducted in collaboration with a wellness care group, which serves the healthy agingneeds of low-income housing residents. Prior to this feasibility study, we conducted a pilot study to collect channel state informationdata from several activity scenarios (eg, sitting, walking, and preparing meals) using the proposed Wi-Fi sensing system continuouslyover a week in apartments of low-income housing residents. These activities were videotaped to generate ground truth annotationsto test the accuracy of the ML algorithms derived from the proposed system. Using qualitative individual interviews, we exploredthe acceptability of the Wi-Fi sensing system and implementation barriers in the low-income housing setting. We use the samestudy protocol for the proposed feasibility study. Results: The Wi-Fi sensing system deployment began in November 2022, with participant recruitment starting in July 2023.Preliminary results will be available in the summer of 2025. Preliminary results are focused on the feasibility of developing MLmodels for Wi-Fi sensing-based activity and mobility assessment, community-based recruitment and data collection, groundtruth, and older adults'Wi-Fi sensing technology acceptance. Conclusions: This feasibility study can make a contribution to SmHT science and ML capabilities for early detection of cognitivedecline among socially vulnerable older adults. Currently, sensing devices are not readily available to this population due to cost and information barriers. Our sensing device has the potential to identify individuals at risk for cognitive decline by assessingtheir level of physical function by tracking their in-home activities and mobility behaviors, at a low cost. International Registered Report Identifier (IRRID): DERR1-10.2196/53447
