Using while moving" is the basic ability fieldwork users require of a mobile computer system.These users come from a wide range of backgrounds but have in common an extremely mobile and dynamic workplace.We identify four specific characteristics of this class of users: dynamic user configuration, limited attention capacity, high-speed interaction, and context dependency.A prototype is then presented that was designed to assist fieldworkers in data collection tasks and to explore the HCI design issues involved.The prototype was used in an extensive field trial by a group of ecologists observing giraffe behavior in Kenya.Following this trial, improvements were made to the prototype interface which in turn was tested in a subsequent field trial with another group of ecologists.From this experience, we have formulated our resulting ideas about interface design for fieldworkers into two general principles: Minimal Attention User Interfaces (MAUIs) and context awareness.The MAUI seeks to minimize the attention, though not necessarily the number of interactions, required from the user in operating a device.Context awareness enables the mobile device to provide assistance based on a knowledge of its environment.

The role of expert systems in species identification, with particular reference to the problems posed by damaged specimens and inexperienced taxonomists, is discussed. Of the three main types of expert systems available, the frame-based system is shown to provide the most appropriate model for a taxonomic expert system rather than a logic- or rule-based system. The advantages of an expert system over other computer-aided methods of identification are considered. A rule-based system requires the original knowledge (species descriptions) to be structured into rules, whereas a frame-based system can store the generic and specific descriptions in a series of frames. The frames fall into a hierarchy which closely resembles the taxonomic hierarchy, and down which information can be inherited. Two aspects of frame-based systems considered are the use of probabilities in identification, and the optimum structure of the knowledge base. The conventional use of probabilities is to provide an indication of the correctness of the result. However, in some studies involving the identification of many specimens, the speed of identification may be increased (with a reduction in accuracy) if identifications are made to a predetermined probability level. Although frames allow accurate representation of the taxonomic hierarchy, a semantic net, incorporating structures of the organism and/or details of the habitat may result in a more efficient expert system.