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Tracking activities holds great potential to improve the well-being of older adults, yet the accuracy of activity trackers for this demographic remains in question. Evaluating this accuracy requires ground-truth data directly from older adults, which has largely been gathered in controlled laboratory settings or labeled by researchers. Moreover, considering the diversity in older adults' activity engagement and tracking preferences, personalized activity tracking appears necessary. We demonstrate that older adults can benefit from personalized activity trackers by showing that cadence thresholds for stepping intensities vary within this group. However, collecting ground-truth data from older adults in real-world settings poses unique challenges. This paper examines two sources of ground-truth labels for the smartwatch Inertial Measurement Unit (IMU) data collected with older adults. Using verbal self-reports and a thigh-worn activity tracker, we assess their viability as ground-truth sources in natural settings. Additionally, we evaluate the costs and benefits of triangulating these sources as a ground-truth proxy. Our findings reveal two main costs: data shrinkage and notable effort from both contributors and data stewards. Simultaneously, we observe improved data quality and a greater ability to identify error sources when evaluating a trained model. 

We study the solvability of singular Abreu equations which arise in the approximation of convex functionals subject to a convexity constraint. Previous works established the solvability of their second boundary value problems either in two dimensions, or in higher dimensions under either a smallness condition or a radial symmetry condition. Here, we solve the higher-dimensional case by transforming singular Abreu equations into linearized Monge–Ampère equations with drifts. We establish global Hölder estimates for linearized Monge–Ampère equations with drifts under suitable hypotheses, and then apply them to prove the regularity and solvability of the second boundary value problem for singular Abreu equations in higher dimensions. Many cases with general right-hand side are also discussed. 

Activity tracking has the potential to promote active lifestyles among older adults. However, current activity tracking technologies may inadvertently perpetuate ageism by focusing on age-related health risks. Advocating for a personalized approach in activity tracking technology, we sought to understand what activities older adults find meaningful to track and the underlying values of those activities. We conducted a reflective interview study following a 7-day activity journaling with 13 participants. We identified various underlying values motivating participants to track activities they deemed meaningful. These values, whether competing or aligned, shape the desirability of activities. Older adults appreciate low-exertion activities, but they are difficult to track. We discuss how these activities can become central in designing activity tracking systems. Our research offers insights for creating value-driven, personalized activity trackers that resonate more fully with the meaningful activities of older adults. 

The local explanation provides heatmaps on images to explain how Convolutional Neural Networks (CNNs) derive their output. Due to its visual straightforwardness, the method has been one of the most popular explainable AI (XAI) methods for diagnosing CNNs. Through our formative study (S1), however, we captured ML engineers' ambivalent perspective about the local explanation as a valuable and indispensable envision in building CNNs versus the process that exhausts them due to the heuristic nature of detecting vulnerability. Moreover, steering the CNNs based on the vulnerability learned from the diagnosis seemed highly challenging. To mitigate the gap, we designed DeepFuse, the first interactive design that realizes the direct feedback loop between a user and CNNs in diagnosing and revising CNN's vulnerability using local explanations. DeepFuse helps CNN engineers to systemically search unreasonable local explanations and annotate the new boundaries for those identified as unreasonable in a labor-efficient manner. Next, it steers the model based on the given annotation such that the model doesn't introduce similar mistakes. We conducted a two-day study (S2) with 12 experienced CNN engineers. Using DeepFuse, participants made a more accurate and reasonable model than the current state-of-the-art. Also, participants found the way DeepFuse guides case-based reasoning can practically improve their current practice. We provide implications for design that explain how future HCI-driven design can move our practice forward to make XAI-driven insights more actionable. 

Speech as a natural and low-burden input modality has great potential to support personal data capture. However, little is known about how people use speech input, together with traditional touch input, to capture different types of data in self-tracking contexts. In this work, we designed and developed NoteWordy, a multimodal self-tracking application integrating touch and speech input, and deployed it in the context of productivity tracking for two weeks (N = 17). Our participants used the two input modalities differently, depending on the data type as well as personal preferences, error tolerance for speech recognition issues, and social surroundings. Additionally, we found speech input reduced participants' diary entry time and enhanced the data richness of the free-form text. Drawing from the findings, we discuss opportunities for supporting efficient personal data capture with multimodal input and implications for improving the user experience with natural language input to capture various self-tracking data. 

Current activity tracking technologies are largely trained on younger adults’ data, which can lead to solutions that are not well-suited for older adults. To build activity trackers for older adults, it is crucial to collect training data with them. To this end, we examine the feasibility and challenges with older adults in collecting activity labels by leveraging speech. Specifically, we built MyMove, a speech-based smartwatch app to facilitate the in-situ labeling with a low capture burden. We conducted a 7-day deployment study, where 13 older adults collected their activity labels and smartwatch sensor data, while wearing a thigh-worn activity monitor. Participants were highly engaged, capturing 1,224 verbal reports in total. We extracted 1,885 activities with corresponding effort level and timespan, and examined the usefulness of these reports as activity labels. We discuss the implications of our approach and the collected dataset in supporting older adults through personalized activity tracking technologies.