We are identifying people's activities while browsing the Web solely by analyzing their mouse movement behavior. We use machine-learning algorithms to develop systems that predict user activities and user interest using algorithms that correlate mouse tracks and implicit metrics for activity and interest. Clustering low-level mouse data into a relatively small set of features is vital in tracking people�s behaviors as they occur. Our systems offer a trade-off between model, feature, and computational complexity to be suitable for online usage.