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International audienceFree-space gestural systems are faced with two major issues: a lack of subtlety due to explicit mid-air arm movements, and the highly effortful nature of such interactions. With an ever-growing ubiquity of interactive devices, displays, and appliances with non-standard interfaces, lower-effort and more socially acceptable interaction paradigms are essential. To address these issues, we explore at-one's-side gestural input. Within this space, we present the results of two studies that investigate the use of side-gesture input for interaction. First, we investigate end-user preference through a gesture elicitation study, present a gesture set, and validate the need for dynamic, diverse, and variable-length gestures. We then explore the feasibility of designing such a gesture recognition system, dubbed WatchTrace, which supports alphanumeric gestures of up to length three with an average accuracy of up to 82%, providing a rich, dynamic, and feasible gestural vocabulary

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Bi-level thresholding is a motion gesture recognition technique that mediates between false positives, and false negatives by using two threshold levels: a tighter threshold that limits false positives and recognition errors, and a looser threshold that prevents repeated errors (false negatives) by analyzing movements in sequence. In this paper, we examine the effects of bi-level thresholding on the workload and acceptance of endusers. Using a wizard-of-Oz recognizer, we hold recognition rates constant and adjust for fixed versus bi-level thresholding. Given identical recognition rates, we show that systems using bi-level thresholding result in significant lower workload scores on the NASA-TLX and accelerometer variance. Overall, these results argue for the viability of bi-level thresholding as an effective technique for balancing between false positives, recognition errors and false negatives.

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