Evaluating the Scalability of Non-Preferred Hand Mode Switching in Augmented Reality
| Autor: | Isaac Wang, Julia Woodward, Winston Wei, Jesse Smith, Jaime Ruiz |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
05 social sciences Work (physics) 02 engineering and technology Range (mathematics) Scalability 0202 electrical engineering electronic engineering information engineering Electronic engineering Mode switching 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Augmented reality Scaling 050107 human factors Gesture |
| Zdroj: | AVI |
| DOI: | 10.1145/3399715.3399850 |
| Popis: | Mode switching allows applications to support a wide range of operations (e.g. selection, manipulation, and navigation) using a limited input space. While the performance of different mode switching techniques has been extensively examined for pen- and touch-based interfaces, investigating mode switching in augmented reality (AR) is still relatively new. Prior work found that using non-preferred hand is an efficient mode switching technique in AR. However, it is unclear how the technique performs when increasing the number of modes, which is more indicative of real-world applications. Therefore, we examined the scalability of non-preferred hand mode switching in AR with two, four, six, and eight modes. We found that as the number of modes increase, performance plateaus after the four-mode condition. We also found that counting gestures have varying effects on mode switching performance in AR. Our findings suggest that modeling mode switching performance in AR is more complex than simply counting the number of available modes. Our work lays a foundation for understanding the costs associated with scaling interaction techniques in AR. |
| Databáze: | OpenAIRE |
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