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Unravelling seams: improving mobile gesture recognition with visual feedback techniques

Gesture recognition is emerging as an engaging interaction technique in mobile scenarios, and high recognition rates promote user acceptance. Several factors influence recognition rates including the nature of the gesture set and the suitability of the gesture recognition algorithm. This work explores how seamfulness in gesture stroke visualization affects recognition rates. We present the results of a user evaluation of a gesture recognition system that shows that raw (seamful) visualization of low-delity gesture stroke data has recognition rates comparable to no feedback. Providing filtered (seamless) stroke visualization to the user, while retaining the un-filtered input data for recognition, resulted in a 34.9% improvement in gesture recognition rate over raw stroke data. The results provide insights into the broader design space of seamful design, and identifies areas where seamlessness is advantageous.