Performance Predictive Model Research Of 3D User Interface On Handheld Mobile Devices

Menus are key interaction components for interfaces of handheld mobile devices. As the number of functions of handheld mobile devices keeps increasing, the contradiction between the growing menu scale and the limited screen size is getting more and more severe. As 3D (three-dimensional) interface technology can extend interface information capacity, adopting 3D menu for handheld mobile device interaction is worth researching. To this end, effective means for evaluating corresponding user performance need to be found. There are two different approaches to be used to evaluate user performance:one approach is the laboratory-based usability testing, the other one is the model-based prediction methods. Compared with the laboratory-based usability testing, the advantage of the model-based prediction methods is that researchers can evaluate user interfaces faster and lower cost by using it. Therefore, we attempt to adopt the model-based prediction methods to evaluate 3D menu performance on handheld mobile devices. Operation time and operation error rate are important part of user performance. So we proposed two prediction models:one model for operation time, and the other model for operation error rate.Firstly, we proposed a modified information-processing model for handheld mobile device menu operation. The purpose is to provide support for the research behind. Information-processing model is evolved from cognitive psychology. It described the process of human brain processing information. The process is divided into several stages chronological order:Sensory Memory, Perception, Decide and Select Response, Response Control. We put forward three factors which affect menu performance:menu dimension, task complexity, and user knowledge structure. By analyzing these factors influence on all stages of information-processing model, we modified the model so that it can describe the information processing of handheld mobile device menu operation.On the basis of the information-processing model for handheld mobile device menu operation, we proposed a prediction model for operation time of 3D menu on handheld mobile devices based on Fitts law and Hick-Hyman law. For the needs of pre- dictting operation time of 3D menu on handheld mobile devices, we modified the Fitts law according to the characteristics of 3D user interface so that it can better predict the time. Fitts law describes the movement time taken to acquire a visual target, and thus ignores the time taken to find it. This problem can be resolved through Hick-Hyman law. So we take it into account in our model. Besides, in order to predict the operation time of different types of users (such as expert users, novice users), we take the users' expertise level into account in our model.Then we proposed a prediction model for error rate of 3D menu on handheld mobile devices based on Fitts law and Speed-Accuracy Tradeoff. From the related work of Fitts law, we know that Fitts law itself is not a prediction model for users'operation error rate. But it mathematically implies a predictive error rate model. Therefore, we modified the Fitts law according to the Speed-Accuracy Tradeoff so that it can play the ability to predict error rate. We proposed the predictive error rate model for lD user interfaces firstly. Then we used the same methods to propose the predictive error rate model for 2D user interfaces and 3D user interfaces. So we got the predictive error rate model for 3D menu on handheld mobile devices.Lastly, we designed experiments to test whether the two models above were valid. The experiments were carried out in the standard usability laboratory.20 users (10 male and 10 female) were recruited to participate in the experiments and within-participant testing was used. Regression analysis of the observed data through experiment and the predicted data through the models yields a high correlation.The two predictive models can help researchers evaluate user interfaces quickly, thus shorten the evaluation period. At the same time, the two predictive models can reduce the cost of the evaluation. They provide a rapid and effective method for researchers of user interface.