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Effect of Motion-Gesture Recognizer Error Pattern on User Workload and Behavior

Keiko Katsuragawa 1 Ankit Kamal 1 Edward Lank 1, 2
2 MJOLNIR - Computing tools to empower users
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : 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 end-users. 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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https://hal.inria.fr/hal-01654868
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Submitted on : Monday, December 4, 2017 - 2:39:55 PM
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Keiko Katsuragawa, Ankit Kamal, Edward Lank. Effect of Motion-Gesture Recognizer Error Pattern on User Workload and Behavior. IUI 2017 - 22nd annual meeting of the Intelligent User Interfaces community, Mar 2017, Limassol, Cyprus. pp.439-449, ⟨10.1145/3025171.3025234⟩. ⟨hal-01654868⟩

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