A Study of Inherent Pen Input Modalities for Precision Parameter Manipulations during Trajectory Tasks

Abstract

SUMMARYAdjustment 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). key words: human computer interaction, pen input, inherent pen input modalities, multi-scale navigation, pen-based interfaces

A pen pressure division method using personal distribution

“…Subjects performed fastest using PU method, and performed slowest using EV method. Post hoc pairwise comparisons found significant difference (p < .05) between all nLevels pairs except (7, 8) (p = 0.28), (8,9) (p = 0.14), and (10, 11) (p = 0.67). A repeated measures analysis of variance showed that block had a significant effect on selection time (F 2, 16 = 13.42, p < .001).…”

“…Ren et al [8] proposed the Adaptive Hybrid Cursor to facilitate target selection performance by automatically adjusting cursor size according to pen pressure. Xin and Ren [9] implemented a widget to manipulate high precision parameters. They comparatively investigated the performance of inherent pen input modalities (pen pressure, tilt, azimuth, and rolling) used for precision parameter manipulation during pen sliding actions and found that pen azimuth exhibited the worst performance.…”