Body gesture recognition using crow search algorithm enhanced probabilistic neural network for human- computer interaction [version 1; peer review: awaiting peer review]

• Published in: F1000 research Vol. 14; p. 149
• Main Authors: V.S, BAKKIALAKSHMI, Chawengsaksopark, Pornpimol, Sathiyanarayanan, Mithileysh
• Format: Journal Article
• Language: English
• Published: 2025
• Subjects: Crow Search Algorithm; Watershed Algorithm; Optimized Probabilistic Neural Network; Human-Computer Interaction; Body Gesture Recognition
• ISSN: 2046-1402; 2046-1402
• DOI: 10.12688/f1000research.160816.1

Background Body gesture recognition has become a fundamental technique in Human-Computer Interaction (HCI). As human-machine interaction evolves, there is an increasing need for precise and efficient gesture detection systems. However, current methods face limitations such as accuracy constraints, high computational complexity, and limited adaptability. This study addresses these challenges by proposing an innovative approach to enhance the accuracy and efficiency of body gesture recognition systems. Methods The proposed system integrates advanced algorithms and techniques to improve performance. A Marker-Based Watershed Algorithm is employed for accurate image segmentation, enhancing region detection. Feature extraction uses a Convolutional Neural Network (CNN), while a Wavelet Transform-Based Pre-Processing technique improves input data quality. A unique component of this method is the application of the Crow Search Algorithm to optimize model efficiency. An Optimized Probabilistic Neural Network (PNN) is utilized for gesture classification, aiming to increase precision and computational effectiveness. Results The proposed approach achieves a gesture recognition accuracy rate of 99%. Compared to traditional methods such as Decision Trees (DT), Support Vector Machines (SVM), and Improved Neural Networks (INN), the Optimized PNN demonstrates a 2.21% improvement in overall accuracy. The implementation, carried out in Python, showcases the robustness and adaptability of the system across diverse HCI applications. Conclusions This work presents a comprehensive solution to the challenges of body gesture recognition by integrating cutting-edge algorithms. Combining the Marker-Based Watershed Algorithm, CNN-based feature extraction, and Crow Search Optimization significantly enhances the system's accuracy and efficiency. By addressing the shortcomings of existing methods, this approach provides a more responsive, reliable, and flexible gesture recognition system, contributing to the advancement of HCI technologies. The results demonstrate the potential for improved human-computer interaction through more effective and precise gesture detection.