| 1. |
- Le, Khanh Duy, 1989, et al.
(author)
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Towards Leaning Aware Interaction with Multi-touch Tabletops
- 2016
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In: 9th Nordic Conference on Human-Computer Interaction, NordiCHI 2016, Gothenburg, Sweden, 23-27 October 2016. - New York, NY, USA : ACM. - 9781450347631 ; 23-27-October-2016
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Conference paper (peer-reviewed)abstract
- Interactive tabletops allow direct touch manipulation and recognizing simultaneous touch events. Users sometimes lean on the touch surface creating unintended touch input. Our work demonstrates how this unintended input can be employed to enhance interaction. In a study we develop a posture set organized into four classes. We present a vision-based machine-learning algorithm using an active shape model to recognize the classes. The algorithm categorizes lean gestures into one of the classes for interaction purposes. In a second study, we evaluate the model and propose interaction scenarios that use lean detection.
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| 2. |
- Alvina, J., et al.
(author)
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OmniVib: Towards cross-body spatiotemporal vibrotactile notifications for mobile phones
- 2015
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In: 33rd Annual CHI Conference on Human Factors in Computing Systems, CHI 2015; Seoul; South Korea; 18 April 2015 through 23 April 2015. - New York, NY, USA : ACM. - 9781450331456 ; 2015-April, s. 2487-2496
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Conference paper (peer-reviewed)abstract
- - Previous research has shown that one's palm can reliably recognize 10 or more spatiotemporal vibrotactile patterns. However, recognition of the same patterns on other body parts is unknown. In this paper, we investigate how users perceive spatiotemporal vibrotactile patterns on the arm, palm, thigh, and waist. Results of the first two experiments indicate that precise recognition of either position or orientation is difficult across multiple body parts. Nonetheless, users were able to distinguish whether two vibration pulses were from the same location when played in quick succession. Based on this finding, we designed eight spatiotemporal vibrotactile patterns and evaluated them in two additional experiments. The results demonstrate that these patterns can be reliably recognized (>80%) across the four tested body parts, both in the lab and in a more realistic context.
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