Hand-Tracking-Based Mouse Control for Touchless Human- Computer Interaction: Feasibility and Future Enhancement

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Year : 2025 | Volume : 13 | Issue : 02 | Page : –
    By

    Sameer Awasthi,

  • Amarnath,

  • Abhay Dixit,

  • Arpit Singh,

  1. Head of Department, Computer Science Engineering in Artificial Intelligence and Machine Learning, Bansal Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India
  2. Student, Computer Science Engineering in Artificial Intelligence and Machine Learning, Bansal Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India
  3. Student, Computer Science Engineering in Artificial Intelligence and Machine Learning, Bansal Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India
  4. Student, Computer Science Engineering in Artificial Intelligence and Machine Learning, Bansal Institute of Engineering & Technology, Lucknow, Uttar Pradesh, India

Abstract

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This paper presents a novel approach to human-computer interaction through a hand- tracking-based mouse control system using computer vision. The system utilizes OpenCV, MediaPipe, and the Cvzone Hand Tracking Module to identify and monitor hand movements in real-time. By utilizing hand landmarks, the system maps finger positions to cursor movement and enables essential functions such as clicking, scrolling, and double-clicking. The primary motivation for developing this system is to create a touchless alternative to traditional input devices, offering enhanced accessibility and usability. The methodology involves capturing video input via a webcam, detecting hands using MediaPipe’s pre-trained models, and implementing a gesture recognition algorithm to interpret user actions. Cursor movements are mapped through an interpolation function, while clicking and scrolling gestures are detected based on finger positions and distances between key landmarks. Performance evaluation indicates an average response time of 20-30ms, ensuring real-time usability. The system achieves an accuracy rate of 95% in controlled lighting conditions. However, challenges such as lighting variations and occlusion remain key areas for improvement. To mitigate unintended gestures, delays and threading mechanisms are incorporated. Future enhancements will focus on improving gesture classification with AI models, addressing lighting-related limitations, and incorporating multi-hand interactions for increased functionality. This research contributes to the field of gesture-based computing, offering a cost-effective, hardware-independent solution for modern human-computer interaction.

Keywords: Hand tracking, Computer Vision, Gesture Recognition, Virtual Mouse, Human-Computer Interaction (HCI).

[This article belongs to Research & Reviews: A Journal of Embedded System & Applications ]

How to cite this article:
Sameer Awasthi, Amarnath, Abhay Dixit, Arpit Singh. Hand-Tracking-Based Mouse Control for Touchless Human- Computer Interaction: Feasibility and Future Enhancement. Research & Reviews: A Journal of Embedded System & Applications. 2025; 13(02):-.
How to cite this URL:
Sameer Awasthi, Amarnath, Abhay Dixit, Arpit Singh. Hand-Tracking-Based Mouse Control for Touchless Human- Computer Interaction: Feasibility and Future Enhancement. Research & Reviews: A Journal of Embedded System & Applications. 2025; 13(02):-. Available from: https://journals.stmjournals.com/rrjoesa/article=2025/view=0



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References

  1. Zhang, X., et al. “Hand Gesture Recognition for Human-Computer Interaction: A Review”. As reported in the 2021 edition of IEEE Transactions on Pattern Analysis and Machine Intelligence.
  2. Wang, J., et al. “A Real-Time Hand Tracking Algorithm for Gesture-Based Interfaces.” Computer Vision and Image Understanding, 2022.
  3. Lee, C., et al. “Hand Pose Estimation Using Deep Learning Models.” Neural Computing and Applications, 2023.
  4. Patel, R., et al. “Comparison of Gesture Recognition Techniques for HCI.” Journal of AI Research, 2021.
  5. Kumar, S., et al. “Advancements in Computer Vision-Based Gesture Control.” Pattern Recognition Letters, 2022.
  6. Kim, D., et al. “Human-Computer Interaction via Hand Tracking.” IEEE Transactions on Multimedia, 2021.
  7. Smith, A., et al. “A Survey on Touchless Interfaces Using Computer Vision.” ACM Computing Surveys, 2022.
  8. Gupta, P., et al. “Deep Learning for Hand Gesture Recognition.” IEEE Access, 2023.
  9. Choi, J., et al. “Gesture-Based Navigation in Virtual Reality.” Multimedia Tools and Applications, 2022.
  10. Li, M., et al. “Applications of Computer Vision in HCI.” Sensors and Actuators B: Chemical, 2021.
  11. Zhang, H., et al. “A Review on Vision-Based Hand Gesture Recognition Systems.” Journal of Visual Communication and Image Representation, 2023.
  12. Fernandez, L., et al. “Improving Gesture Recognition Accuracy with AI Models.” Machine Learning and Applications, 2022.
  13. Torres, R., et al. “A Study on Real-Time Gesture Recognition for Smart Spaces,” featured in the IEEE Internet of Things Journal, 2021.
  14. Ahmed, K., et al. “Gesture-Based Control Systems for Accessibility.” Assistive Technologies Journal, 2023.
  15. Park, S., et al. “Machine Learning Approaches to Gesture Recognition.” Artificial Intelligence Review, 2022.
  16. Nguyen, T., et al. “Enhancing Hand Detection with AI Techniques.” Image and Vision Computing, 2021.
  17. Wu, Y., et al. “Gesture Recognition for Interactive Gaming and AR Experiences,” featured in Entertainment Computing, 2023.
Regular Issue Subscription Review Article
Volume 13
Issue 02
Received 04/03/2025
Accepted 04/04/2025
Published 22/04/2025
Publication Time 49 Days
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