論文アブストラクト： Operating systems support autoscroll to allow users to scroll a view while in dragging mode: the user moves the pointer near the window's edge to trigger an "automatic" scrolling whose rate is typically proportional to the distance between the pointer and the window's edge. This approach suffers from several problems, especially when the window is maximized, resulting in a very limited space around it. Another problem is that for some operations, such as object drag-and-drop, the source and destination might be located in different windows, making it complicated for the computer to understand user's intention. In this paper, we present ForceEdge, a novel autoscroll technique relying on touch surfaces with force-sensing capabilities to alleviate the problems related to autoscroll. We report on the results of three controlled experiments showing that it improves over macOS and iOS systems baselines for top-to-bottom select and move tasks.
論文アブストラクト： For more than two decades, capacitive sensing has played a prominent role in human-computer interaction research. Capacitive sensing has become ubiquitous on mobile, wearable, and stationary devices - enabling fundamentally new interaction techniques on, above, and around them. The research community has also enabled human position estimation and whole-body gestural interaction in instrumented environments. However, the broad field of capacitive sensing research has become fragmented by different approaches and terminology used across the various domains. This paper strives to unify the field by advocating consistent terminology and proposing a new taxonomy to classify capacitive sensing approaches. Our extensive survey provides an analysis and review of past research and identifies challenges for future work. We aim to create a common understanding within the field of human-computer interaction, for researchers and practitioners alike, and to stimulate and facilitate future research in capacitive sensing.
論文アブストラクト： We present a novel, paired, wearable system for combining the kinesthetic experiences of two persons. These devices allow users to sense and combine muscle contraction and joint rigidity bi-directionally. This is achieved through kinesthetic channels based on electromyogram (EMG) measurement and electrical muscle stimulation (EMS). We developed a pair of wearable kinesthetic input-output (I/O) devices called bioSync that uses specially designed electrodes to perform biosignal measurement and stimulation simultaneously on the same electrodes.In a user study, participants successfully evaluated the strength of their partners' muscle contractions while exerting their own muscles. We confirmed that the pair of devices could help participants synchronize their hand movements through tapping, without visual and auditory feedback. The proposed interpersonal kinesthetic communication system can be used to enhance interactions such as clinical gait rehabilitation and sports training, and facilitate sharing of physical experiences with Parkinson's patients, thereby enhancing understanding of the physical challenges they face in daily life.
論文アブストラクト： Quantifying cumulative arm muscle fatigue is a critical factor in understanding, evaluating, and optimizing user experience during prolonged mid-air interaction. A reasonably accurate estimation of fatigue requires an estimate of an individual's strength. However, there is no easy-to-access method to measure individual strength to accommodate inter-individual differences. Furthermore, fatigue is influenced by both psychological and physiological factors, but no current HCI model provides good estimates of cumulative subjective fatigue. We present a new, simple method to estimate the maximum shoulder torque through a mid-air pointing task, which agrees with direct strength measurements. We then introduce a cumulative fatigue model informed by subjective and biomechanical measures. We evaluate the performance of the model in estimating cumulative subjective fatigue in mid-air interaction by performing multiple cross-validations and a comparison with an existing fatigue metric. Finally, we discuss the potential of our approach for real-time evaluation of subjective fatigue as well as future challenges.