The past several decades have seen significant advances in monitoring older adults' health and well-being. However, creating viable, practical monitoring systems for informing caregivers requires understanding which behaviors and signs to track and what approaches best present that information. To investigate how technology can be leveraged to better augment caregivers' workflows, we take a multi-stage, qualitative approach to gain insights into the needs of caregivers and the older adults receiving care. Specifically, we use a series of domain expert interviews, cognitive walkthroughs, and semi-structured interviews with residents, and we synthesize our takeaways using thematic analysis at each phase. Our results show that this type of monitoring technology has great potential to reduce the effort needed by caregivers to complete their responsibilities and communicate with their teams. Additionally, we found that older adults are receptive to the technology but their privacy and autonomy must be prioritized for the sake of their mental wellbeing. These insights will facilitate greater intelligent interface development for Person-Centered Care by identifying important design considerations and vital features that require system support.

Increased virtual meeting software usage has allowed people to meet remotely in a more seamless fashion. However, compared to in-person meetings, valuable interaction cues such as impromptu group polling are less optimally executed due to increased difficulty in gauging remote participants, while also requiring prior meeting setup for automated counting with built-in polling tools. We propose a novel intelligent user interface approach for virtual meeting software that supports impromptu polling interactions by leveraging real-time hand gesture recognition and video filter feedback. We conducted studies to design and evaluate this intuitive gesture-based polling system with visual feedback. Our results demonstrated that our system was able to recognize attendees’ gestures and poll responses with reasonable accuracy, and showed improvements in hosts’ task workload performance. From our findings, our interface informs hosts of valuable results while maintaining organic gestural interaction cues with attendees similar to in-person meetings.

This paper explores my doctoral research work on supporting how people express their embodied virtual meeting interactions through the synthesis of physical gesticulations and video filters. The goal is to suggest novel interaction methods that first capture the intent from people’s communicated gestures, and then express those intentions more effectively to other virtual meeting attendees as informative video filter responses. By combining interpreted physical gestures as input and informative video filters as output, I hope my research can bring a greater dimension in how people express their embodied interactions beyond the constraints of current virtual meeting environments.

Kanji script writing is a skill that is often introduced to novice Japanese foreign language students for achieving Japanese writing mastery, but often poses difficulties to students with primarily English fluency due to their its vast differences with written English. Instructors often introduce various pedagogical methods—such as visual structure and written techniques—to assist students in kanji study, but may lack availability providing direct feedback on students' writing outside of class. Current educational applications are also limited due to lacking richer instructor-emulated feedback. We introduce Kanji Workbook, a writing-based intelligent tutoring system for students to receive intelligent assessment that emulates human instructor feedback. Our interface not only leverages students' computing devices for allowing them to learn, practice, and review the writing of prompted characters from their course's kanji script lessons, but also provides a diverse set of writing assessment metrics—derived from instructor interviews and classroom observation insights—through intelligent scoring and visual animations. We deployed our interface onto novice- and intermediate-level university courses over an entire academic year, and observed that interface users on average achieved higher course grades than their peers and also reacted positively to our interface's various features.

Recent trends in computer-mediated communication (CMC) have not only led to expanded instant messaging through the use of images and videos but have also expanded traditional text messaging with richer content in the form of visual communication markers (VCMs) such as emoticons, emojis, and stickers. VCMs could prevent a potential loss of subtle emotional conversation in CMC, which is delivered by nonverbal cues that convey affective and emotional information. However, as the number of VCMs grows in the selection set, the problem of VCM entry needs to be addressed. Furthermore, conventional means of accessing VCMs continue to rely on input entry methods that are not directly and intimately tied to expressive nonverbal cues. In this work, we aim to address this issue by facilitating the use of an alternative form of VCM entry: hand gestures. To that end, we propose a user-defined hand gesture set that is highly representative of a number of VCMs and a two-stage hand gesture recognition system (trajectory-based, shape-based) that can identify these user-defined hand gestures with an accuracy of 82%. By developing such a system, we aim to allow people using low-bandwidth forms of CMCs to still enjoy their convenient and discreet properties while also allowing them to experience more of the intimacy and expressiveness of higher-bandwidth online communication.