Predictive Cursor Behavior System for Enhanced User Interaction

Abstract

This paper presents a predictive system designed to enhance user interaction through real-time cursor behavior analysis and forecasting. By leveraging cursor movement data and applying machine learning techniques such as recurrent neural networks, the system learns user patterns to predict future cursor positions. The predictive model enables proactive interface adjustments, including cursor assistance and automated actions. This innovation holds potential for improving efficiency, accessibility, and intuitiveness across web browsing, productivity tools, and assistive technologies.

Introduction

This research focuses on predicting mouse cursor movements in real time to anticipate user intentions, enhancing user interfaces, accessibility, and productivity. By modeling cursor trajectories, the system bridges the gap between user intent and system response, which is especially beneficial in gaming, accessibility, and adaptive UI scenarios.

Related Work:
Traditional methods like Kalman filters and support vector regression struggle with temporal dependencies and non-linearities in cursor data. Recent approaches using LSTM networks effectively capture sequential patterns, but many lack real-time inference or interactive feedback. This work integrates LSTM-based prediction within a GUI, providing live visual feedback on prediction performance.

Methodology:
The system has five stages: data collection, preprocessing, feature engineering, model training, and real-time prediction. Mouse positions and click events are recorded, filtered, and converted into features like velocity, direction, acceleration, and time delta. Sequences of 100 time steps are input to a 3-layer LSTM network predicting the next cursor position. The model is trained with MSE loss and Adam optimizer and deployed in a GUI that visualizes actual vs. predicted movements in real time.

Results:
The model achieves an average RMSE of 7.42 pixels, with prediction rates around 20 Hz. Predictions closely follow actual cursor trajectories, with slight deviations during fast or erratic motion. The GUI successfully visualizes these predictions and provides real-time statistics.

Applications:

• Web browsing: pre-loading elements, auto-highlighting links

• Gaming: predictive targeting and control automation

• Accessibility: reducing effort for users with motor impairments

• UI/UX optimization: adaptive responses to predicted user intent

Conclusion

The Predictive Cursor Behavior System demonstrates the feasibility of using deep learning to enhance human-computer interaction. Through LSTM-based prediction and interactive GUI visualization, the system offers a step toward anticipatory interfaces. With further refinement, it can be adopted in various applications ranging from accessibility aids to intelligent UIs and gaming enhancements.

References

[1] H. Zhang, M. Liu, and D. Wang, \"A High-Performance General Computer Cursor Control Scheme Based on Eye-Tracking and Deep Learning,\" Heliyon, vol. 10, no. 3, 2024, Art. e13980. [2] J. Lee and S. Kim, \"Is Mouse Dynamics Information Credible for User Behavior Research?,\" Information Processing & Management, vol. 61, no. 2, 2024, Art. 103198. [3] M. Varela, C. Lin, and K. Wang, \"Exploring Visual Representations of Computer Mouse Movements for User Behavior Analysis,\" Expert Systems with Applications, vol. 209, 2023, Art. 118211. [4] Z. Li, X. Xu, and J. Zhou, \"Predicting Mouse Click Position Using Long Short-Term Memory Model Trained by Joint Loss Function,\" in Proc. IEEE Int. Conf. on Machine Learning and Cybernetics (ICMLC), 2021, pp. 123–128. [5] J. Lu and M. Gales, \"Learning Efficient Representations of Mouse Movements to Predict User Attention,\" in Proc. ACM Symp. Eye Tracking Research & Applications (ETRA), 2020, pp. 1–8. M. Liu, and D. Wang, \"A High-Performance General Computer Cursor Control Scheme Based on Eye-Tracking and Deep Learning,\" Heliyon, vol. 10, no. 3, 2024, Art. e13980.

Copyright

Copyright © 2025 Prof. Ramkrishna Bharsakade, Vandan Sharma , Shriniwas Wanjare, Sujal Varat, Vedant Varude , Vedant Baraskar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.