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.

From an early age, children begin developing critical motor skills, such as fine motor control, that contribute significantly to reading, writing, drawing, and more, all of which are important for communication and school readiness. Pediatricians can evaluate a child’s motor skills using activities and questionnaires. Sometimes these involve adults drawing with their child, but it can be difficult to fully evaluate a child’s drawings through a handful of sketches from limited direct assessments. We propose creating a sketching system that will collect free-form drawing data from parents and children that can then automatically differentiate a child’s sketch from an adult’s using only the pen strokes of their drawing. In this paper, we describe our study that collected sketches from 14 children aged 2 to 5 and 25 adults over 18. We contribute a machine learning classifier based on sketch recognition features from free-hand drawings capable of distinguishing children’s sketches from those made by adults with an F-measure of 0.906. These results indicate the potential of creating sketch-based applications for assessing children’s fine motor development.

This Research Work In Progress Paper examines empirical evidence on the impacts of feedback from an intelligent tutoring software on sketching skill development. Sketching is a vital skill for engineering design, but sketching is only taught limitedly in engineering education. Teaching sketching usually involves one-on-one feedback which limits its application in large classrooms. To meet the demands of feedback for sketching instruction, SketchTivity was developed as an intelligent tutoring software. SketchTivity provides immediate personalized feedback on sketching freehand practice. The current study examines the effectiveness of the feedback of SketchTivity by comparing students practicing with the feedback and without. Students were evaluated on their motivation for practicing sketching, the development of their skills, and their perceptions of the software. This work in progress paper examines preliminary analysis in all three of these areas.

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.

The shift to virtual has changed our society and left an impact on nearly every part of our lives. Although it brought many challenges, global remote access also opened up a world of educational and professional opportunities for many people who did not have them before. In this work, we detail the findings of a study collecting feedback on inclusivity in virtual, in-person, and hybrid spaces, with the goal of building a greater understanding of the issues and personal challenges faced by access and equity stakeholders. From a survey of 104 individuals and detailed interviews with 12, we have used a mix of qualitative and quantitative methods to discover key challenges. The diversity of experiences and opinions was striking, with many winners and losers going into the virtual space. We propose some modifications to the online environment and in-person practices with the aim of furthering equality.

Sketchtivity is a stylus-based intelligent tutoring system that can help instructors automatically provide feedback to their students, saving them the time and effort of providing personalized feedback themselves. The system uses a generic evaluation of perspective, direction, and accuracy to give students feedback on the quality of their sketches. If instructors want to personalize the metrics, the system would require them to provide multiple sets of samples. Therefore, instructors may use instructional team members such as teaching and graduate teaching assistants to provide feedback on the required samples. Compared to that of assistants, the feedback they produce might vary due to expertise and create noise in the training data. To address this problem, we implement a deep neural network that leverages learning to reweight algorithms. The data collected by the instructor from undergraduate and graduate-level rectilinear perspectives sketching is considered the validated sample. In this study, we analyzed the training size requirement for a Multi-Layer Perceptron (MLP) to accurately predict whether or not a stroke was a perspective stroke. We observed that the training data required to predict stroke accuracy is small. In addition, the performance of the algorithm in terms of accuracy was good even under extreme conditions such as having highly unbalanced data and having a small valid set of data. The results from the study support the use of these types of algorithms for future system personalizing to support scalable feedback systems in education.

With the onset of the COVID-19 pandemic, faculty are suddenly thrown into a world where they have to teach virtually, forcing them to innovate in their teaching practice without the ability to “chat with their colleagues” next door. Additionally, many faculty do not know how to create an inclusive classroom, which is crucial for students who have also lost their learning community and support structure. Many faculty are afraid to include issues of inclusion and diversity, feeling ill equipped and unsupported. As tensions rise across the US, there is a critical need for engineering students to be able to discuss issues involving inclusion in the classroom as well as apply their abilities as engineers to make a significant impact (both positively or negatively) on inclusion through their applications and innovations. Without the proper support for the faculty to innovate and foster inclusive classrooms, millions of students will suffer in their education. Additionally, without a trusting support group, most engineering faculty would not choose to assume the risk. In response, we developed a community of practice with six teaching fellows who shared, watched, commented on, and emulated each other's classroom recordings. The teaching fellows learned a significant amount from each other, courageously implemented activities focused on inclusion and awareness in their classrooms, observed greater awareness among their students on inclusion issues, and were able to mentor faculty and publish about the data driven best practices that they completed.

Nearly half of people prescribed medication to treat chronic or short-term conditions do not take their medicine as prescribed. This leads to worse treatment outcomes, higher hospital admission rates, increased healthcare costs, and increased morbidity and mortality rates. While some instances of medication non-adherence are a result of problems with the treatment plan or barriers caused by the health care provider, many are instances caused by patient-related factors such as forgetting, running out of medication, and not understanding the required dosages. This presents a clear need for patient-centered systems that can reliably increase medication adherence. To that end, in this work we describe an activity recognition system capable of recognizing when individuals take medication in an unconstrained, real-world environment. Our methodology uses a modified version of the Bagging ensemble method to suit unbalanced data and a classifier trained on the prediction probabilities of the Bagging classifier to identify when individuals took medication during a full-day study. Using this methodology we are able to recognize when individuals took medication with an F-measure of 0.77. Our system is a first step towards developing personal health interfaces that are capable of providing personalized medication adherence interventions.