Personalized Productivity Prediction Using Desktop Activity

Abstract

Productivity monitoring has become an essential feature of modern digital work environments. The traditional methods of measuring productivity involve manual monitoring and evaluation, which may not correctly reflect the real productivity of the users. This paper suggests a personalized system of productivity prediction based on desktop activity monitoring. The system monitors the activities of the users, such as the use of applications, typing, mouse movement, working hours, etc., and then uses machine learning algorithms to predict the level of productivity of the users. The proposed system can provide personalized insights about the patterns of users, which can be helpful for understanding their productivity. The main focus of the proposed system is to identify patterns in computer usage that determine how well one performs at work. The constant analysis of user interaction with the computer allows for trends in productivity to be identified, which would be extremely useful in improving how users utilize their computers. The integration of machine learning algorithms allows the system to be dynamic in accommodating different user patterns, providing personalized information rather than generalized levels of productivity. In addition, it would be useful in identifying time-wasting activities to prevent user distractions during working hours. The proposed system can also be utilized in a remote working environment, where the challenge of monitoring user productivity is significant. The experimental analysis proves that the proposed system can effectively utilize the available desktop activity to estimate the levels of user productivity with reasonable accuracy. The proposed system can offer a powerful solution to the intelligent assessment of user productivity in the modern digital working environment. There is a potential for such a model to be used for decision-making processes for individuals and organizations, considering that it has the potential to offer data-driven information on productivity. The model can be extended to include workplace analytics systems, which can be used to enhance the workflow and digital management of tasks in the workplace. The results indicate the potential benefits that can be obtained by incorporating behavioral data and intelligent algorithms in designing a more adaptive and efficient productivity monitoring system.