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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.

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Design sketching is an important skill for designers, engineers, and creative professionals, as it allows them to express their ideas and concepts in a visual medium. Being a critical and versatile skill for many different disciplines, courses on design sketching are often taught in universities. Courses today predominately rely on pen and paper; however, this traditional pedagogy is limited by the availability of human instructors, who can provide personalized feedback. Using a stylus-based intelligent tutoring system called SketchTivity , we aim to eventually mimic the feedback given by an instructor and assess student-drawn sketches to give students insight into areas for improvement. To provide effective feedback to users, it is important to identify what aspects of their sketches they should work on to improve their sketching ability. After consulting with several domain experts in sketching, we came up with several classes of features that could potentially differentiate expert and novice sketches. Because improvement on one metric, such as speed, may result in a decrease in another metric, such as accuracy, the creation of a single score may not mean much to the user. We attempted to create a single internal score that represents overall drawing skill so that the system can track improvement over time and found that this score correlates highly with expert rankings. We gathered over 2,000 sketches from 20 novices and four experts for analysis. We identified key metrics for quality assessment that were shown to significantly correlate with the quality of expert sketches and provide insight into providing intelligent user feedback in the future.