Three experiments were conducted in this study to investigate the human ability to control pen pressure and pen tilt input, by coupling this control with cursor position, angle and scale. Comparisons between pen pressure input and pen tilt input have been made in the three experiments. Experimental results show that decreasing pressure input resulted in very poor performance and was not a good input technique for any of the three experiments. In "Experiment 1-Coupling to Cursor Position", the tilt input technique performed relatively better than the increasing pressure input technique in terms of time, even though the tilt technique had a slightly higher error rate. In "Experiment 2-Coupling to Cursor Angle", the tilt input performed a little better than the increasing pressure input in terms of time, but the gap between them is not so apparent as Experiment 1. In "Experiment 3-Coupling to Cursor Scale", tilt input performed a little better than increasing pressure input in terms of adjustment time. Based on the results of our experiments, we have inferred several design implications and guidelines.

An experiment was conducted to measure and compare the physiological effects of three types of CRT on users. We proposed a new strategy for measuring the user's level of relaxation. In this strategy, called "Task Break Monitoring (TBM)," the subjects took a break with eyes closed after each interaction with the computer. During each break, electroencephalogram (EEG), especially alpha 1 waves, electrocardiogram (ECG) and galvanic skin resistance (GSR) were monitored and recorded. The results show that the type of CRT display which emits far-infrared rays modulated by a FIR-fan induce less fatigue in users while they are working and reduce the recovery time after the task was completed. We believe "TBM" to be an important innovation in human computer research and development because the after effects of computer use have an obvious bearing on recovery time, user endurance and psychological attitude to the technology in general etc.

Continuous pen pressure can be used to operate multi-state widgets such as menus in pen based user interfaces. The number of levels into which the pen pressure space is divided determines the number of states in the multi-state widgets. To increase the optimal number of divisions of the pen pressure space and achieve greater pen pressure usability, we propose a new discretization method which divides the pen pressure space according to a personal pen pressure use profile. We present here four variations of the method: discretization according to personal/aggregation pen pressure use profile with/without visual feedback of uniform level widths and the traditional even discretization method. Two experiments were conducted respectively to investigate pen pressure use profile and to comparatively evaluate the performance of these methods. Results indicate that the subjects performed fastest and with the fewest errors when the pen pressure space was divided according to personal profile with visual feedback of uniform level widths (PU) and performed worst when the pen pressure space was divided evenly. With PU method, the optimal number of divisions of the pen pressure space was 8. Visual feedback of uniform level widths enhanced performance of uneven discretization. The findings of this study have implications for human-oriented pen pressure use in pen pressure based user interface designs.

In human-computer interaction, Fitts' law has been applied in one-dimensional pointing task evaluation for some decades, and the usage of effective target width (We) in Fitts' law has been accepted as an international standard in ISO standards 9241-9 [4]. However, the discussion on the concrete methods for calculating We has not been developed comprehensively nor have the different methods of calculation been integrated. Therefore, this paper focuses on a detailed description and a comparison of the two main We calculation methods. One method is mapping all the abscissa data in one united relative coordinate system to perform the calculation (called CC method) and the other is dividing the data into two groups and mapping them in two separate coordinate systems (called SC method). We tested the accuracy of each method and compared both methods in a highly controlled experiment. The experiments' results and data analysis show that the CC method is better than the SC method for human computer interface modeling. These results will be instrumental for future application of Fitts' law.

Fitt's law is commonly used to model target selection. But Fitts' law deals with only one kind of selection strategy. Our question is, do changes in target size, distance and direction to a target affect the differences in performance between target selection strategies? We performed the first empirical tests on a pen-based system to evaluate differences in performance between six selection strategies for selecting a target. Three target sizes, eight pen-movement-directions and three pen-movement-distances were applied to all six strategies. The results show that differences between selection strategies are affected by variations in target size but not by the other parameters (distance and direction).

Although research into multimodal interfaces has been around for a long time, we believe that some basic issues have not been studied yet, e.g. the choice of modalities and their combinations is usually made without any quantitative evaluation. This study seeks to identify the best combinations of modalities through usability testing. How do users choose different interaction modes when they work on a particular application? Two experimental evaluations were conducted to compare interaction modes on a CAD system and a map system respectively. For the CAD system, the results show that, in terms of total manipulation time (drawing and modification time) and subjective preferences, the "pen + speech + mouse" combination was the best of the seven interaction modes tested. On the map system, the results show that the "pen + speech" combination mode is the best of fourteen interaction modes tested. The experiments also provide information on how users adapt to each interaction mode and the ease with which they are able to use these modes.

An experiment is reported comparing six pen input strategies for selecting a small target using five diffenent sized targets (1, 3, 5, 7 and 9 dot diameter circles respectively, 0. 36 mm per dot). The results showed that the best strategy, in terms of error rate, selection time and subjective preferences, was the "land-on2" strategy where the target is selected when the pen-tip touches the target for the first time after landing on the screen surface. Moreover, "the smallest maximum size" was determined to be 5 dots (1. 8 mm). This was the largest size among the targets which had a significant main effect on error rate in the six strategies. These results are important for both researchers and designers of pen-based systems.

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).

Direct manipulation by hand is an intuitive and simple way of positioning objects in an immersive virtual environment. However, this technique is not suitable for making precise adjustments to virtual objects in an immersive environment because it is difficult to hold a hand unsupported in midair and to then release an object at a fixed point. We therefore propose an alternative technique using a virtual 3D gearbox widget that we have designed, which enables users to adjust values precisely. We tested the technique in a usability study along with the use of hand manipulation and a slider. The results showed that the gearbox was the best of the three techniques for precise adjustment of small targets, in terms of both performance data and subject preference.

Fitts' law has been applied in many studies to evaluate pointing tasks. However, the quantitative effect of using color in the interfaces has not been discussed in the literature. This paper introduces research on the effects of color in pointing tasks using Fitts' law as the evaluation method. Different colors and color presentation styles are applied in the experiments which are similar in design to the paradigmatic Fitts' law pointing task. The experimental results show that when the subjects use a mouse as the input device, there is no significant difference between an interface with a colored target and one with a white target in the mean performance time. The results also reveal that color presentation styles will offer no significant difference to pointing tasks when the mouse is applied. However, when the interface of tablet PC and pen was applied, subjects without much experience in tablet personal computer usage needed more time to perform the task with colored targets than with a white target. Furthermore, when the colors are changed randomly during the selection process, the difference is even more obvious. These results are confirmed by a Checking Experiment and a Learning Effect Experiment which we performed on different groups of subjects.