Controlling Media Player Through Hand Gesture Recognition System Using CNN and RNN Models

Year : 2024 | Volume :15 | Issue : 01 | Page : 29-34
By

    Khallikkunaisa

  1. Mayank N. Nandwani

  2. Rohit Chauhan

  3. Prabhant Kumar

  4. Nitesh Kumar

  1. Associate Professor, Computer Science and Engineering, HKBK College of Engineering, Bengaluru, Karnataka, India
  2. Student, Computer Science and Engineering, HKBK College of Engineering, Bengaluru, Karnataka, India
  3. Student, Computer Science and Engineering, HKBK College of Engineering, Bengaluru, Karnataka, India
  4. Student, Computer Science and Engineering, HKBK College of Engineering, Bengaluru, Karnataka, India
  5. Student, Computer Science and Engineering, HKBK College Of Engineering, Bengaluru, Karnataka, India

Abstract

Artificial intelligence markup language (AIML) project represents a pioneering endeavor in the realm of media player control through hand gesture recognition, merging advanced technologies like convolutional neural networks (CNN) and recurrent neural networks (RNN). By harnessing the image analysis capabilities of CNN, our system ensures accurate, real-time detection, and interpretation of intricate hand gestures, enabling users to interact with their media content naturally and seamlessly. What sets our project apart is the incorporation of RNN, which imbues the system with a temporal understanding of gestures, enhancing its ability to recognize complex sequences of gestures and commands, including actions like play, pause, or nuanced volume adjustments. This synergy of CNN and RNN not only exemplifies the transformative potential of AI and deep learning in human-computer interaction but also promises to redefine the user experience, offering an accessible, responsive, and immersive means of controlling digital entertainment. Our project addresses a critical need for more intuitive user interfaces, particularly for individuals with physical limitations, making media playback more inclusive and engaging.

Keywords: Hand gesture recognition, convolutional neural network (CNN), recurrent neural network (RNN), human-computer interaction, inclusive user interfaces

[This article belongs to Journal of Computer Technology & Applications(jocta)]

How to cite this article: Khallikkunaisa, Mayank N. Nandwani, Rohit Chauhan, Prabhant Kumar, Nitesh Kumar.Controlling Media Player Through Hand Gesture Recognition System Using CNN and RNN Models.Journal of Computer Technology & Applications.2024; 15(01):29-34.
How to cite this URL: Khallikkunaisa, Mayank N. Nandwani, Rohit Chauhan, Prabhant Kumar, Nitesh Kumar , Controlling Media Player Through Hand Gesture Recognition System Using CNN and RNN Models jocta 2024 {cited 2024 Apr 05};15:29-34. Available from: https://journals.stmjournals.com/jocta/article=2024/view=140190


References

  1. Nagalapuram GD, Roopashree S, Varshashree D, Dheeraj D, Nazareth DJ. Controlling media player with hand gestures using convolutional neural network. In: 2021 IEEE Mysore Sub Section International Conference (MysuruCon), Hassan, India, October 24–25, 2021. pp. 79–86.
  2. Islam S, Matin A, Kibria HB. Hand gesture recognition based human computer interaction to control multiple applications. In: Vasant P, Zelinka I, Weber GW, editors. Intelligent Computing & Optimization. ICO 2021. Lecture Notes in Networks and Systems, volume 371. Cham, Switzerland: Springer; pp. 397–406.
  3. Sen A, Mishra TK, Dash R. Deep learning-based hand gesture recognition system and design of a human machine interface. Neural Process Lett. 2023; 55: 12569–12596.
  4. Paliwal M, Sharma G, Nath D, Rathore A, Mishra H, Mondal S. A dynamic hand gesture recognition system for controlling VLC media player. In: 2013 International Conference on Advances in Technology and Engineering (ICATE), Mumbai, India, January 23–25, 2013. pp. 1–4.
  5. Liu X, Chen T. Video-based face recognition using adaptive hidden Markov models. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA, June 18–20, 2003. pp. 1–1.
  6. Nagalapuram GD, Roopashree S, Varshashree D, Dheeraj D, Nazareth DJ. Controlling media player with hand gestures using convolutional neural network. IEEE Mysore Sub Section International Conference (MysuruCon). 2021. pp. 79–86. doi: 10.1109/MysuruCon52639.2021.9641567.
  7. Chaman S, Jani J, Fernandes H, Dhuka R, Mehta D. Real-time gesture to automotive control. In: 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), Coimbatore, India, March 1–3, 2018. pp. 1–6.
  8. Jalab H, Omer HK. Human-computer interface using hand gesture recognition based on the neural network. In: 2015 5th National Symposium on Information Technology: Towards New Smart World (NSITNSW), Riyadh, Saudi Arabia, February 17–19, 2015. pp. 1–6.
  9. Niranjani V, Keerthana R, Mohana Priya B, Nekalya K, Padmanabhan AK. System application control based on hand gesture using deep learning. In: 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, March 19–20, 2021. pp. 1644–1649.
  10. Selvarathi C, Indu P, Kavyadarshini B, Logesh Kumar S, Mohamed Yasher R. Human computer interaction using hand gesture recognition. Int J Adv Trends Computer Sci Eng. 2020; 9 (2): 1600–1603. doi: 10.30534/ijatcse/2020/106922020.
  11. Abhishek B, Krishi K, Meghana M, Daaniyaal M, Anupama HS. Hand gesture recognition using machine learning algorithms. Computer Sci Inform Technol. 2020; 1 (3): 116–120.
  12. Veronica PG, Mokkapati RK, Jagupilla LP, Santhosh C. Static hand gesture recognition using novel convolutional neural network and support vector machine. Int J Online Biomed Eng. 2023; 19 (9): 131–141.
  13. Vásconez JP, Barona López LI, Valdivieso Caraguay ÁL, Benalcázar ME. Hand gesture recognition using EMG-IMU signals and deep Q-networks. Sensors. 2022; 22 (24): 9613.
  14. Shetty S, Joseph J, Thomas JS, Umesh MK, Vaishnav E. Hand gesture recognition system to control keyboard functions. Int J Creative Res Thoughts. 2023; 11 (5): b73–b78.
  15. Shinde S, Mushrif S, Pardeshi A, Jagtap D, Vandana Rupnar P. Gesture based media player controller. Int J Res Publ Rev. 2022; 3 (5): 2289–2294.
  16. Toro-Ossaba A, Jaramillo-Tigreros J, Tejada JC, Peña A, López-González A, Castanho RA. LSTM recurrent neural network for hand gesture recognition using EMG signals. Appl Sci. 2022; 12 (19): 9700.
  17. Haria A, Subramanian A, Asokkumar N, Poddar S, Nayak JS. Hand gesture recognition for human computer interaction. Procedia Computer Sci. 2017; 115: 367–374.
  18. Kim AR, Rhee SY. Intention estimation of a walker around pedestrian lights by using fuzzy rules. In: 2012 International Conference on Fuzzy Theory and Its Applications (iFUZZY2012), Taichung, Taiwan, November 16–18, 2012. pp. 233–237.
  19. Lai K, Yanushkevich SN, Shmerko V. Reliability of decision support in cross-spectral biometric-enabled systems. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Toronto, Ontario, Canada, October 11–14, 2020. pp. 3401–3406.
  20. Hua Y, Alahari K, Schmid C. Online object tracking with proposal selection. In: Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, December 7–13, 2015. pp. 3092–3100.
  21. Nunez JC, Cabido R, Pantrigo JJ, Montemayor AS, Velez JF. Convolutional neural networks and long short-term memory for skeleton-based human activity and hand gesture recognition. Pattern Recogn. 2018; 76: 80–94.
  22. Agrawal R, Gupta N. Real time hand gesture recognition for human computer interaction. In: 2016 IEEE 6th International Conference on Advanced Computing (IACC), Bhimavaram, India, February 27–28, 2016. pp. 470–475.
  23. Sahoo JP, Prakash AJ, Pławiak P, Samantray S. Real-time hand gesture recognition using fine-tuned convolutional neural network. Sensors. 2022;22:706. doi: 10.3390/s22030706. PubMed: 35161453.
Regular Issue Subscription Review Article
Volume 15
Issue 01
Received February 22, 2024
Accepted February 28, 2024
Published April 5, 2024
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