The aim of this study is to propose a unit touch gesture model, which would be useful to predict the performance time on mobile devices. When measuring performance time based on model-based evaluation (MBE), a Keystroke Level Model (KLM) used ‘operators’ to predict the execution time in the desktop ...
The aim of this study is to propose a unit touch gesture model, which would be useful to predict the performance time on mobile devices. When measuring performance time based on model-based evaluation (MBE), a Keystroke Level Model (KLM) used ‘operators’ to predict the execution time in the desktop environment. For this study, these unit touch gestures, like operators in Keystroke Level Model (KLM), would be determined to predict performance time for mobile devices under dynamic environment. In order to extract unit touch gestures, representative touch gestures (Tap, Drag, Rotate, Flick, Pinch, Spread) were selected and recorded at the 120 frames/sec with pixel coordinates. Then, each movement was analysed based on the following criteria; ‘out of range’, registration’, continuation’, and ‘termination’ of gesture. Depending on run-time aspect and movement time, we extracted unit touch gestures. Six unit touch gestures are hold down (H; 54msec), release (R; 54msec), slip (S; 123msec), curved-stroke (Cs; 620msec), path-stroke (Ps; 544msec) and out of range (Or; 221msec). In conclusion, the unit touch gesture model could be proven to be effective to predict performance time because no significant difference with experimental data (p-value=0.05). With the six unit gestures, the performance time to any touch gestures could be predicted by simply summing up unit gestures for touch gestures without measuring the movement time of the touch gesture.
The aim of this study is to propose a unit touch gesture model, which would be useful to predict the performance time on mobile devices. When measuring performance time based on model-based evaluation (MBE), a Keystroke Level Model (KLM) used ‘operators’ to predict the execution time in the desktop environment. For this study, these unit touch gestures, like operators in Keystroke Level Model (KLM), would be determined to predict performance time for mobile devices under dynamic environment. In order to extract unit touch gestures, representative touch gestures (Tap, Drag, Rotate, Flick, Pinch, Spread) were selected and recorded at the 120 frames/sec with pixel coordinates. Then, each movement was analysed based on the following criteria; ‘out of range’, registration’, continuation’, and ‘termination’ of gesture. Depending on run-time aspect and movement time, we extracted unit touch gestures. Six unit touch gestures are hold down (H; 54msec), release (R; 54msec), slip (S; 123msec), curved-stroke (Cs; 620msec), path-stroke (Ps; 544msec) and out of range (Or; 221msec). In conclusion, the unit touch gesture model could be proven to be effective to predict performance time because no significant difference with experimental data (p-value=0.05). With the six unit gestures, the performance time to any touch gestures could be predicted by simply summing up unit gestures for touch gestures without measuring the movement time of the touch gesture.
주제어
#Unit touch gesture mobile device Model-based Evaluation (MBE) Keystroke Level Model Usability Touch gesture HCI (Human-Computer Interaction)
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