3D Motion Gesture Control : Gesture Recognition and Adaptation for Human Computer Interaction

Authors

  • Anuja Phapale AISSMS Institute Of Institute Of Information Technology
  • Shriya Sawashe AISSMS Institute Of Institute Of Information Technology

DOI:

https://doi.org/10.59890/ijaamr.v2i1.730

Keywords:

3D Gesture Recognition, Hand Gesture Recognition, , HCI, Augmented reality, Virtual Reality

Abstract

Advancements in human-computer interaction (HCI) have paved the way for more intuitive and immersive interfaces. The first part of the paper delves into the fundamental principles of 3D gesture recognition, including sensor technologies, machine learning algorithms, and computer vision techniques. It discusses the challenges associated with accurate recognition in various environmental conditions and the ways in which these challenges are being addressed by researchers. The second part focuses on the adaptation aspect of the technology. It highlights how 3D gesture recognition can be integrated into adaptive HCI systems, enabling personalized and context-aware interactions. These adaptations can range from adjusting the interface layout to suit the user's preferences to dynamically changing the system's behavior based on the user's gestures. Additionally, the paper discusses the potential applications of 3D gesture recognition in fields such as gaming, virtual reality, healthcare, and beyond. It emphasizes the need for continued research to improve accuracy, robustness, and user-friendliness, ultimately driving the widespread adoption of 3D gesture recognition in HCI.

References

Blackburn, Reinforcement Learning: Markov-Decision Process (Part 1). Towards Data Science. Accessed: Sep. 12, 2022

C. Zhu, J. Yang, Z. Shao, and C. Liu, ‘‘Vision based hand gesture recognition using 3D shape context,’’ IEEE/CAA J. Automat. Sinica, vol. 8, no. 9, pp. 1600–1613, Sep. 2021, doi: 10.1109/JAS.2019. 1911534

D. Liu, L. Zhang, and Y. Wu, ‘‘LD-ConGR: A large RGB-D video dataset for long-distance continuous gesture recognition,’’ in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2022, pp. 3294–3302, doi: 10.1109/CVPR52688.2022.00330.

Develop. (ICICT SD), Feb. 2021, pp. 450–455, doi: 10.1109/ICICT4SD50815.2021.9396879.

H. Mansoor, N. Kalra, P. Goyal, M. Bansal, and N. Wadhwa, ‘‘Hand gesture recognition using 3D CNN and computer interfacing,’’ in Inventive Systems and Control, vol. 436, V. Suma, Z. Baig, S. K. Shanmugam, and P. Lorenz, Eds. Singapore: Springer, 2022. G. Zhang, What is the Kernel Trick? Why is it Important? Towards Data Science. Accessed: Sep. 12, 2022.

K. M. Hasib, M. A. Habib, N. A. Towhid, and M. I. H. Showrov, ‘‘A novel deep learning based sentiment analysis of Twitter data for U.S. Airline service,’’ in Proc. Int. Conf. Inf. Commun. Technol. Sustain.

M. M. Kabir, A. Q. Ohi, and M. F. Mridha, ‘‘A multi-plant disease diagnosis method using convolutional neural network,’’ in Computer Vision and Machine Learning in Agriculture. Singapore: Springer, 2021, pp. 99–111

N. S. Suriani, and S. I. Suliman, ‘‘Translating hand gestures using 3D convolutional neural network,’’ Int. J. Academic Res. Bus. Social Sci., vol. 12, no. 6, Jun. 2022.

R. Kwok, Baum-Welch Algorithm for Training a Hidden Markov Model. Medium. Accessed: Sep. 12, 2022.

Shawn Hickey. (Aug. 31, 2022). Kinect for Windows. Microsoft. Accessed: Sep. 12, 2022.

Downloads

Published

2024-01-25

How to Cite

Anuja Phapale, & Shriya Sawashe. (2024). 3D Motion Gesture Control : Gesture Recognition and Adaptation for Human Computer Interaction. International Journal of Applied and Advanced Multidisciplinary Research, 2(1), 23–30. https://doi.org/10.59890/ijaamr.v2i1.730