This paper describes the Web-based Biometric Mouse Intelligent (WBMI) System developed by the authors for measuring and analysis of user's emotions and labour productivity with a biometric mouse. The research included development of the WBMI System, which works in the background and is able to assess user's emotional state and labour productivity during work with a computer. The system captures information about user's emotional state and labour productivity using three main biometric techniques: physiological (skin conductance, amplitude of hand tremble, skin temperature), psychological (e-self-reports) and behavioural/motor-behavioural (mouse pressure, speed of mouse pointer movement, acceleration of mouse pointer movement, scroll wheel turns, right-and left-click frequency). The system extracts physiological and motorbehavioural parameters from mouse actions and palm characteristics, and the user fills in the psychological (e-self-reports) data, which can be used to analyse correlations with user's emotional state and labour productivity. Main features of the WBMI System are discussed, and the final recommendations for future research and improvement are included.

In order to increase the efficiency of employees' activities, a Web-based Biometric Mouse Decision Support System for User's Emotional and Labour Productivity Analysis (MDSS-UELPA) was developed by the authors. MDSS-UELPA consists of seven subsystems: Data Capture and Collection Subsystem, Feature Extraction Subsystem, Database Management Subsystem, Model-base Management Subsystem, Equipment Subsystem, e-Self-Reports Subsystem and Graphical Interface. MDSS-UELPA is an online system and allows for more physiological, psychological and behavioural data to be generated from a larger pool of users for further analysis and research. Data is accumulated in individual user modules based on the user's mouse movements, palm state and e-self-reports. The basic assumption was that MDSS-UELPA could successfully model user behaviours on the basis of the above-mentioned physiological, psychological and behavioural data. MDSS-UELPA is briefly analysed in this paper.