Authors:
Gnaneswari Gnanaguru, Edwin Shalom Soji, S. Belina V. J. Sara, M. Rehena Sulthana, C. Christina Angelin
Addresses:
Department of Computer Applications, CMR Institute of Technology, Bangalore, Karnataka, India. Department of Computer Science, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India. Department of Computer Applications, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India. School of Information Technology and Engineering, Melbourne Institute of Technology, Melbourne, Victoria, Australia. Department of Mathematics, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India.
Abstract:
Skeleton data from human postures and movements reveals much for enhancing action and gesture detection systems. This study explores methods that optimise data use, improving recognition performance. Our approach uses skeletal data to enable more intuitive and seamless human-machine interactions in healthcare, entertainment, security, and other fields. Computer vision and human-computer interaction benefit from skeleton data. Our research develops novel methods to enhance action and gesture detection systems by utilizing skeletal information. These methods enable AI systems to perceive and interpret human gestures, facilitating more natural and successful interactions. Many applications are possible with this research. Patient monitoring and rehabilitation can benefit from gesture recognition. More immersive virtual reality experiences are possible in entertainment. It improves security surveillance and threat detection. AI and skeletal data can enhance the lives and safety of individuals and communities. This research shows how skeletal data can transform action and gesture recognition. Optimizing data use enables more natural and seamless human-machine interactions across various areas, thereby enhancing the quality of life and safety for individuals and communities.
Keywords: Skeleton Data; Action Recognition; Gesture Recognition; Accuracy and Efficiency; Human-Machine Interaction; Techniques and Applications; Computer Vision; Significant Enhancement.
Received: 01/04/2024, Revised: 19/05/2024, Accepted: 20/07/2024, Published: 01/03/2025
DOI: 10.64091/ATICL.2025.000092
AVE Trends in Intelligent Computer Letters, 2025 Vol. 1 No. 1 , Pages: 11-20