User Modcl to Design Adaptabie Interfaces for Motor-Impaired Users Pradipta Biswas School of Information Technology Indian Institute of Technology, Kharagpur WB-721302, India pbiswas@,sit. iitkgp. ernet. in Samit Bhattacharya Department of Computer Sc. & Engg. Indian Institute of Technology, Kharagpur WB-721302, India samit@,cse. iitkgp. ernet. in Debasis Samanta School of Information Technology Indian Institute of Technology, Kharagpur WB-721302, India debasis. samanta@,ieee. org Abstract- User modeling is an important strategy for designing effective user interface. User model for able-bodied user is not always suitable for physically and mentally retarded people. There are some special characteristics of users and also of the interfaces, which have to be considered when building a user model for disabled users. This paper concerns about modeling motor-impaired users for developing personalized interfaces for those people. In the proposed user model, main emphasis is given on making the user model application independent, clustering users according to their physical disability and cognitive level and adapting the model with respect to individual user as well as cluster of users. I. INTRODUCTION User is the most important component in human computer interaction design. Users possess a great challenge to the HCI designer because of a large variety of user profiles based on task, situation and user characteristics itself. So HCI centers on understanding of the users. Understanding users include both knowing the users and tracking user's behavior. In general, understanding user's mental model is a crucial part in any software design. The concept of mental model is more elaborated in HCI and gave birth to user model. User model is the explicit assumption about the knowledge and mentality of a user. A lot of works have been done on user modeling for varieties of applications. In [1], a user model, namely, Generative User Model for information retrieval is discussed. In this model, given a user's query, it relates to the user's mental state and retrieved objects using latent probabilistic variables. In [2], fuzzy logic is used to classify users of an intelligent tutoring system. The fuzzy groups are used to derive certain characteristic of the user and thus deriving new rules for each class of users. In [3], artificial neural network is used for the same purpose as in [1]. The user's characteristic is stored as user image and neural networks are used as pattern associates or pattern classifiers to get user's knowledge, detect user's goal etc. Lumiere convenience project [4] of ASC group of Microsoft research pioneered another probabilistic model, viz. influence diagram in modeling users. Lumiere project is the background theory of the Office Assistant shipped with Microsoft Office application. The influence diagram shows the relationships among user's acute needs, goals, user background etc. When the users are not able-bodied normal user, the design of user model becomes more difficult. Some implicit assumption in case of able-bodied users has to be taken explicitly for disabled users. For example, the intellectual level of able-bodied users is assumed in accordance to their age, but it is not true for mentally retarded users. AVANTI [5] project aims to address the interaction requirements of disabled individuals for a web-based multimedia application. The distinctive characteristic of the user interface in AVANTI is its capability to dynamically tailor itself to the abilities, skills, requirements and preferences of the users, to the different contexts of use, as well as to the changing characteristics of the users, while interacting with the system. The categories of disabled users supported in the current version of the system are: people with light, or severe motor disabilities, and blind people. The user model is based on static and dynamic characteristics of the users. The user interface of AVANTI project first gets initialized according to some static characteristics of the user. After getting started, the interface keeps changing its behavior depending on some dynamic characteristics of the user. There is a decision mechanism, which triggers certain actions to modify the interface depending on the dynamic behaviors of the users. There are some other software available commercially, such as EZ-Keys [6], Clicker [7] etc. The user models used for query prediction or information retrieval [1] [3] are very much application dependent. Only few works have been reported on modeling disabled users. AVANTI project models disabled users but its application is on developing a web browser. Further, there is no significant work reported on clustering disabled users based on cognitive level. In [8], Card & Moran's Model Human Processor (MHP) is used as a user model. Different parameters of this model like perceptual response time, cognitive response time etc. are measured using special techniques for motor-impaired users and the metrics are compared with that of able-bodied users. The largest difference is observed in the motor function time. The MHP model is found to give good insight for understanding interaction of motor-impaired users with computer. However, this model is very simple and no discussion has been provided about how the model can be used for making inferences useful for an actual interface design. In [9], an informative discussion is presented on a data-logging tool (BASE) for disabled users and also the analysis technique