This workshop accepted full research papers and extended abstract submissions. The full research papers will appear as part of the official workshop proceedings and will be available for download through the ACM Digital Library. We will provide links to full research papers when they are published in the ACM Digital Library. While the extended abstracts are not included in the official ACM workshop proceedings, they are available for download below.
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%.
Abstract: Background: Nonadherence to medication is a growing problem across all demographics, particularly older adults who are the most likely to be taking multiple medications. Nonadherence has yet to be solved by digital health technologies. Forgetfulness is one of the main contributors to unintentional nonadherence, which hundreds of medication management apps and devices seek to address by generating time-based reminders. However, most medications are prescribed to be taken within a time frame, such as “in the morning” or “before breakfast,” rather than at a specific hour. Similarly, many components of daily living occur within time frames rather than exact times. Timed reminders do not always correlate with prescription instructions nor daily life; this mismatch may lead to a patient's lack of success in reducing forgetfulness. In domains other than medication prescribed “as-needed,” instead of timed reminders, have been successfully deployed. Borrowing this notion may lead to medication reminders that are accepted and responded to, reducing forgetfulness and leading to healthier aging in the home. Objective: The purpose of this study was to evaluate the utility of time-based reminders to increase medication adherence in older adults taking medication and who desire to safely 'age in place.'
Abstract: Utilizing fine grained analysis of wireless signals for human activity recognition has gained a lot of traction recently. The unique changes to the ambient wireless signals caused by different activities made it possible to recognize these fingerprints through deep learning classification methods. Most of the existing work consider a set of physical activities or gestures and try to recognize each one of them as a separate class. However, this makes the classification task harder especially when the number of activities to recognize becomes larger and when these activities include movements from the same body parts. To address that, in this study, we consider the decomposition of each physical activity into the limbs and body parts involved in that activity and study a one-by-one recognition solution. We propose a Generative Adversarial Network (GAN)-based hierarchical method that not only recognizes the involved body limbs and facilitates the recognition of complex activities, but also mitigates the temporal effects in the collected signal data and thus provides a generalized solution. Our experimental evaluation shows that we can recognize unknown physical activities through the proposed hierarchical limb recognition based model with a small Hamming loss and by just using WiFi signal data from a single transmitter and receiver link.
Abstract: Mobile health (mHealth) apps have gained popularity over the past decade for patient health monitoring, yet their potential for timely intervention is underutilized due to limited integration with electronic health records (EHR) systems. Current EHR systems lack real-time monitoring capabilities for symptoms, medication adherence, physical and social functions, and community integration. Existing systems typically rely on static, in-clinic measures rather than dynamic, real-time patient data. This highlights the need for automated, scalable, and human-centered platforms to integrate patient-generated health data (PGHD) within EHR. Incorporating PGHD in a user-friendly format can enhance patient symptom surveillance, ultimately improving care management and post-surgical outcomes. To address this barrier, we have developed an mHealth platform, ROAMM-EHR, to capture real-time sensor data and Patient Reported Outcomes (PROs) using a smartwatch. The ROAMM-EHR platform can capture data from a consumer smartwatch, send captured data to a secure server, and display information within the Epic EHR system using a user-friendly interface, thus enabling healthcare providers to monitor post-surgical symptoms effectively.
Abstract: Diabetic foot ulcers (DFUs) represent a significant global health challenge for the elderly with high mortality rates and complications. While imaging technologies like NIRS and hyperspectral imaging have improved wound assessment in clinical settings, their cost, and large size limit their use in the home and primary care. On the other hand, existing mobile solutions only capture secondary bio-markers like color and wound size. This paper introduces SigmoidOxy (or σ(Oxy)), a novel smartphone-based perfusion tool for DFU management. SigmoidOxy extracts oxygenation information from standard RGB images captured by smartphone cameras by applying hyperspectral reconstruction models to infer oxygenation. We evaluate SigmoidOxy's performance using the SPECTRALPACA dataset finding an Average Persons R of 0.72 and Average Mean Absolute Error of 0.239 when comparing sigmoid oxygenation signals and analyze its sensitivity to ischemia in the DFUC2021 dataset.
Abstract: Self-tracking technologies hold great potential for supporting individuals' health and wellness goals. They are of great interest for gathering older adults' health data. Our understanding of how these technologies can support older adults with memory concerns is limited, as is our knowledge of how to design them to accommodate cognitive changes. Our analysis of interviews with older people with concerns about their memory yielded an understanding of some forms of work that self-technologies technologies create that are not related to issues having to do with a single self-tracking application. Rather, the issues we describe here come from integrating self- tracking technologies with each other and into one's life. Understanding and mitigating these factors may contribute to more successful technology design.
Abstract: In this extended abstract, through an ethnographic approach involving facilitating AI lectures and engaging in informal discussions, we study ways in which older adults in a living community demonstrated an understanding of and ability to think through the AI landscape. We found older adults provide insightful evaluations of AI based on their rich life experiences and knowledge, as well as understanding of societal systems. We argue that engaging older adults' perspectives should be part of our AI development practices.
Abstract: The aging population represents a growing demographic trend globally, with many countries witnessing a rise in the number of older adults. This shift has spurred the development of accessible infrastructures and increased the demand for healthcare services, particularly for those who wish to receive care at home. Among the innovative solutions emerging to address this need, resident health agents stand out as prominent machine-learning-driven applications designed for domestic settings. These agents enable local collection and analysis of sensor data, facilitating personalized health monitoring. Despite their widespread adoption in various smart health applications, including elderly care and Alzheimer's disease monitoring [2], tailoring these agents to meet individual user profiles and specific application requirements remains a significant challenge.