Yiwen Molly Wang

High-quality labels of activity data with broad representations and real-world variability are key to developing activity recognition models tailored to the needs and characteristics of older adults. However, labeling real-world data presents significant challenges, placing a heavy burden on users to provide high-quality labels while staying engaged in their activities. This paper investigates older adults’ perceptions of providing high-quality labels in the context of training their personalized activity trackers. We conducted a co-design study with 12 older adults to envision the labeling process—describing activity names and time spans—using the teachable machines paradigm as a scaffold. We unpack the contextualized definitions of accuracy, precision, and granularity through a thematic analysis of older adults’ perspectives on activity labeling. Our findings present participants’ preferred strategies for obtaining high-quality activity labels with less burden and intrusiveness, including user-initiated labeling and machine-initiated prompting. We discuss design considerations for future data labeling tools that address discrepancies between user perceptions and technical standards in training personalized activity trackers.

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.

Captions make the audio content of videos accessible and understandable for deaf or hard-of-hearing people (DHH). However, in real-time captioning scenarios, captions alone can be challenging for DHH users to identify the active speaker in a real time in multiple-speaker scenarios. To enhance the accessibility of real-time captioning, we propose Haptic-Captioning which provides real-time vibration feedback on the wrist by directly translating the sound of content into vibrations. We conducted three experiments to examine: (1) the haptic perception (Preliminary Study), (2) the feasibility of the haptic modality along with real-time and non-real-time visual captioning methods (Study 1), and (3) the user experience of using the Haptic-Captioning system in different media contexts (Study 2). Our results highlight that the Haptic-Captioning complements visual captions by improving caption readability, maintaining media engagement, enhancing understanding of emotions, and assisting speaker indication in real-time captioning scenarios. Furthermore, we discuss design implications for the future development of Haptic-Captioning.

Older adults have been hit disproportionally hard by the COVID-19 pandemic. One critical way for older adults to minimize the negative impact of COVID-19 and future pandemics is to stay informed about its latest information, which has been increasingly presented through online interactive visualizations (e.g., live dashboards and websites). Thus, it is imperative to understand how older adults interact with and comprehend online COVID-19 interactive visualizations and what challenges they might encounter to make such visualizations more accessible to older adults. We adopted a user-centered approach by inviting older adults to interact with COVID-19 interactive visualizations while at the same time verbalizing their thought processes using a think-aloud protocol. By analyzing their think-aloud verbalizations, we identified four types of thought processes representing how older adults comprehended the visualizations and uncovered the challenges they encountered with these thought processes. Furthermore, we also identified the challenges they encountered with seven common types of interaction techniques adopted by the visualizations. Based on the findings, we present design guidelines for making interactive visualizations more accessible to older adults.