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Introduction:More than 50% of stroke patients experience limitations in activities of daily living (ADLs). Poor performance in ADLs has been associated with increased risk of rehospitalization after stroke and worsened quality of life. Monitoring and measuring ADLs after stroke can provide valuable insight into patients’ recovery and help identify targeted therapies to improve overall functioning. The objective of this study was to determine the feasibility of using contactless room-based sensors to capture physiological parameters and movement in patients following a stroke and whether these data can identify differences in function compared to individuals with varying ages and health status. Methods:Individuals age 18 and older were recruited to come to a study one-bedroom apartment outfitted with 16 ultrawideband contactless sensors collecting physiological data and movement following a scripted activities of daily living (ADL/IADL) protocol. A study-developed app guided patients through scripted ADLs while measuring the time to completion of each activity. Activities were grouped into simple (brush teeth, wash face, e.g.) and complex activities (food preparation, manage laundry, e.g.). Mean time to completion of all activities and activity groups were compared across 5 participant groups: students, non-student adults, healthy older adults, acute stroke survivors, and chronic stroke survivors. Results:A total of 50 participants completed at least one study visit with an average completion time of 2034 seconds (s). There were significant differences between the time to completion of the visit, ranging from 1772s to 2516s (p<0.05). Stroke survivors required the greatest amount of time (2516s), and the acute stroke survivor took the shortest amount of time to complete the tasks (1771s) followed by the students (1809s). While there was no significant difference between participant groups regarding mean total time to complete simple tasks, there was a significant difference (p<.05) in mean total time to complete complex tasks with chronic stroke survivor taking the longest (242s). Conclusions:These data demonstrate the benefits of monitoring stroke recovery and the potential challenges that long-term stroke survivors have in completing ADLs/IADL. Long term, these data show the feasibility of evaluating differences between ADL/IADL performance and the potential to utilize such data to monitor changes in health status as patients recover from stroke. 

Abstract As Americans live longer, there is a growing need to develop and implement technologies to monitor changes in health, supporting aging in place. We have installed privacy-preserving ultra-wideband wireless sensors in a model home to facilitate sensor and algorithm development for future home deployment. These sensors monitor vital signs and provide localization/tracking and activity recognition. We have developed a protocol to continuously collect and measure activities of daily living, location, and gait. We developed a multi-modal app using videos, audio, and texts to guide individuals and track activity completion time. To protect data privacy, raw sensor data is sent to a research cloud, and identifiable image data is sent to a university-supported HIPAA-compliant Box app. We spent >10 months developing, evaluating, stress testing, and improving our system and cloud infrastructure stability to ensure <1% data loss. We completed 19 sessions with volunteers to test our app and protocol, provide feedback, and continue to update our procedures and app before beginning data collection with individuals post-stroke. Recovery progress in post-stroke study participants will complete 3 study visits over 14 weeks to capture clinical and sensor measurements. We continue to refine our home deployment protocols for older adults with diverse technological knowledge. This presentation highlights our progress in developing and deploying our home monitoring system. We will discuss the challenges and procedures to collect, analyze, and interpret continuous physiologic and movement data in real-time, providing information, knowledge, and insights that will become vital to supporting the health of our expanding aging population. 

By 2050, most adults aged 65 and older in the United States will want to age independently at home, a goal that will strain healthcare resources. Adults aged 50 and older (N = 112) were recruited for study participation between 2018 and 2022. They completed surveys and participated in discussion sessions to explore their needs and opinions regarding smart home sensors. Survey results indicated that older adults’ comfort with smart home sensors increased with their perceived need for monitoring when home alone (OR = 1.46; p = 0.012) or sick/recovering from an illness (OR = 2.21; p < 0.001). When sick compared to when healthy, individuals were 2.65 times more likely to prefer installing multiple sensors in the living room, 1.75 times more likely in the kitchen, 3.66 times more likely in the bedroom, and 3.41 times more likely in the bathroom (p < 0.05). Regarding data sharing, participants were most willing to share information with healthcare providers and family members on a regular basis (80 and 81%, respectively) and 71% on a regular basis or when sick/recovering. Comfort with data sharing with professional caregivers (OR = 1.67; p = 0.0017) and monitoring companies (OR = 1.34; p = 0.030) significantly increased when sick/recovering. Discussion sessions highlighted overwhelming concerns about personal security/privacy, loss of independence, and ethical issues in data collection. Participants emphasized the need for new systems to be flexible, cost-effective, user-friendly, and respectful of user autonomy, accommodating diverse life stages, comfort levels, home environments, income levels, and support structures. Insights are now informing sensor data collection in our model home. Study findings underscore the importance of involving potential users in technology development to create effective and acceptable solutions for aging in place. 

Heart rate, a commonly accessible health data from most wearables, carries rich information of a person’s well-being, yet remains of limited deep health applications, due to the lack of groundtruth of health events and their impact on heart rate patterns. Specifically, standard health analytics usually are designed based on well-modeled health conditions thus known data patterns and rich training data. To bridge the gap, we propose HeartInsightify, an exploratory framework that facilitates the process of deriving health-relevant measurable indicators from longitudinal heart rate data, without any of the above knowledge. HeartInsightify focuses on comparative and qualitative study, using model-free statistical methods such as conformal prediction, to study similarities, perform clustering and detect outliers, and build multi-resolutional data summaries, allowing human experts to efficiently examine and verify their health relevance. We conduct extensive experiments to evaluate HeartInsightify using individuals’ free-living heart rate data collected through Fitbit over 6 years. We illustrate the process of analyzing heart rate data for its health relevance and demonstrate the effectiveness of HeartInsightify. We envision that HeartInsightify lays the groundwork for personalized health analytics with continuous monitoring data from wearables.