A Study of Inherent Pen Input Modalities for Precision Parameter Manipulations during Trajectory Tasks
Adjustment of a certain parameter in the course of performing a trajectory task such as drawing or gesturing is a common manipulation in pen-based interaction, Since pen tip information is confined to x-y coordinate data, such concurrent parameter adjustment is not easily accomplished in devices using only a pen tip. This paper comparatively investigates the performance of inherent pen input modalities (Pressure, Tilt, Azimuth, and Rolling) and Key Pressing with the non-preferred hand used for precision parameter manipulation during pen sliding actions. We elaborate our experimental design framework here and conduct experimentation to evaluate the effect of the five techniques. Results show that Pressure enabled the fastest performance along with the lowest error rate, while Azimuth exhibited the worst performance. Tilt showed slightly faster performance and achieved a lower error rate than Rolling. However, Rolling achieved the most significant learning effect on Selection Time and was favored over Tilt in subjective evaluations. Our experimental results afford a general understanding of the performance of inherent pen input modalities in the course of a trajectory task in HCI (human computer interaction).
inherent pen input modalities pen input human computer interaction multi-scale navigation pen-based interfaces