Research literature has shown that pen tilt is a promising input modality in pen-based interaction. However, the human capability to control pen tilt has not been fully evaluated. This paper systematically investigates the human ability to perform discrete target selection tasks by varying the pen stylus' tilt angle through two controlled experiments: tilt acquiring (Experiment 1) and tilt pointing (Experiment 2). Results revealed a decreasing power relationship between angular width and selection time in Experiment 1. The results of Experiment 2 confirmed that pen tilt pointing can be modeled by Fitts' law. Based on our quantitative analysis, we discuss the human ability to control pen tilt and the implications of pen tilt use. We also propose a taxonomy of pen tilt based interaction techniques and showcase a series of possible pen tilt technique designs.

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Aiming at the complexity of posture recognition with Kinect, a method of posture recognition using distance characteristics is proposed. Firstly, depth image data was collected by Kinect, and threedimensional coordinate information of 20 skeleton joints was obtained. Secondly, according to the contribution of joints to posture expression, 60 dimensional Kinect skeleton joint data was transformed into a vector of 24-dimensional distance characteristics which were normalized according to the human body structure. Thirdly, a static posture recognition method of the shortest distance and a dynamic posture recognition method of the minimum accumulative distance with dynamic time warping (DTW) were proposed. The experimental results showed that the recognition rates of static postures, non-cross-subject dynamic postures and cross-subject dynamic postures were 95.9%, 93.6% and 89.8% respectively. Finally, posture selection, Kinect placement, and comparisons with literatures were discussed, which provides a reference for Kinect based posture recognition technology and interaction design.

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

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