In operating mobile devices such as smartphones and tablets, multi-touch input has been a mainstream choice for years. Multi-touch input allows its users to control the object using gestures. One of the gestural controls, pinching, has become so common due to its intuitive use in any zoomable user interface. While pinch gestures are widely used in HCI, only few studies had been modeling the 2D synchronous pinch task. However, it is practical and useful to develop effective models to predict time performance for such a task because of its popularity.
The movement data to be modelled were from a laboratorial experiment where twenty subjects were recruited to perform pinch tasks. The goal of the task was to fit a circular object into a target area surrounded by two concentric circles. The center and radius of the manipulated object were controlled by the position of the index finger and the span in-between the index finger and the thumb. The task varied with movement shifts (the distance between the starting point to the center of the target area), target diameters (the diameter of the target area), target allowances (the width of the target area), and movement directions (the angle of the movement toward the target area). The task started by placing the index and thumb close together within the starting box in the middle of the interface and ended with the index finger and the thumb being placed at the center of the target area and in-between the concentric circles, respectively. The trajectory of the thumb was sampled during the task and later on used for modeling in the study.
Two scenarios were conducted to understand the time performance of thumb. The first scenario considered the thumb moving independently into the target, while the second one was the thumb following hand movement dependently into target circle and adjusting the finger span between the index finger and itself to the target circle. Three modeling equations were tested in both scenarios to compare six modeling variations. Overall, the second scenario was a better fit to completion time with the greatest R2 of 0.954 (RMSE% = 7.24%) when the modeling variation took account of homing phases of thumb movements. Another contribution in this study was modeling the thumb target position. During a movement to the right side, the thumb will be to the left of the index finger and vice versa. This may happen due to the reason that index finger has a better reach to its target.

In operating mobile devices such as smartphones and tablets, multi-touch input has been a mainstream choice for years. Multi-touch input allows its users to control the object using gestures. One of the gestural controls, pinching, has become so common due to its intuitive use in any zoomable user interface. While pinch gestures are widely used in HCI, only few studies had been modeling the 2D synchronous pinch task. However, it is practical and useful to develop effective models to predict time performance for such a task because of its popularity.
The movement data to be modelled were from a laboratorial experiment where twenty subjects were recruited to perform pinch tasks. The goal of the task was to fit a circular object into a target area surrounded by two concentric circles. The center and radius of the manipulated object were controlled by the position of the index finger and the span in-between the index finger and the thumb. The task varied with movement shifts (the distance between the starting point to the center of the target area), target diameters (the diameter of the target area), target allowances (the width of the target area), and movement directions (the angle of the movement toward the target area). The task started by placing the index and thumb close together within the starting box in the middle of the interface and ended with the index finger and the thumb being placed at the center of the target area and in-between the concentric circles, respectively. The trajectory of the thumb was sampled during the task and later on used for modeling in the study.
Two scenarios were conducted to understand the time performance of thumb. The first scenario considered the thumb moving independently into the target, while the second one was the thumb following hand movement dependently into target circle and adjusting the finger span between the index finger and itself to the target circle. Three modeling equations were tested in both scenarios to compare six modeling variations. Overall, the second scenario was a better fit to completion time with the greatest R2 of 0.954 (RMSE% = 7.24%) when the modeling variation took account of homing phases of thumb movements. Another contribution in this study was modeling the thumb target position. During a movement to the right side, the thumb will be to the left of the index finger and vice versa. This may happen due to the reason that index finger has a better reach to its target.

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