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    Motor memory in HCI
    (ACM SIGCHI, 2020) Patibanda, Rakesh; Semertzidis, Nathan Arthur; Scary, Michaela; La Delfa, Joseph Nathan; Baytaş, Mehmet Aydin; Martin-Niedecken, Anna Lisa; Strohmeier, Paul; Fruchard, Bruno; Leigh, Sang-Won; Mekler, Elisa D.; Nanayakkara, Suranga; Wiemeyer, Josef; Berthouze, Nadia; Kunze, Kai; Rikakis, Thanassis; Kelliher, Aisling; Warwick, Kevin; Van Den Hoven, Elise; Mueller, Florian Floyd; Mann, Steve; N/A; N/A; N/A; N/A
    There is mounting evidence acknowledging that embodiment is foundational to cognition. In HCI, this understanding has been incorporated in concepts like embodied interaction, bodily play, and natural user-interfaces. However, while embodied cognition suggests a strong connection between motor activity and memory, we find the design of technological systems that target this connection to be largely overlooked. Considering this, we are provided with an opportunity to extend human capabilities through augmenting motor memory. Augmentation of motor memory is now possible with the advent of new and emerging technologies including neuromodulation, electric stimulation, brain-computer interfaces, and adaptive intelligent systems. This workshop aims to explore the possibility of augmenting motor memory using these and other technologies. In doing so, we stand to benefit not only from new technologies and interactions, but also a means to further study cognition.
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    Realtime engagement measurement in human-computer interaction
    (Institute of Electrical and Electronics Engineers Inc., 2020) Department of Computer Engineering; Department of Computer Engineering; Department of Computer Engineering; N/A; N/A; N/A; Sezgin, Tevfik Metin; Yemez, Yücel; Erzin, Engin; Türker, Bekir Berker; Numanoğlu, Tuğçe; Kesim, Ege; Faculty Member; Faculty Member; Faculty Member; PhD Student; Master Student; Master Student; Department of Computer Engineering; College of Engineering; College of Engineering; College of Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; 18632; 107907; 34503; N/A; N/A; N/A
    Social robots are expected to understand their interlocutors and behave accordingly like humans do. Endowing robots with the capability of monitoring user engagement during their interactions with humans is one of the crucial steps towards achieving this goal. In this work, an interactive game is designed and implemented, which is played with a robot. During the interaction, the user engagement is monitored in realtime via detection of user gaze, turn-taking, laughters/smiles and head nods from audio-visual data. In the experiments conducted, the realtime monitored engagement is found to be consistent with the humanannotated engagement levels.