Kun Khmer Posture Recognition Using Body Landmarks: A Comparative Experiment

In the context of Kun Khmer, a traditional combat technique in Cambodia, the challenge of correcting trainees' postures and movements is significant due to its impact on performance and outcomes. Moreover, incorrect movements during training can lead to physical injuries. This paper presents a machine learning-based system that aims to recognize Kun Khmer postures and potentially facilitate posture correction. The proposed solution utilizes anatomical landmarks and explores multiple classifiers, including Logistic Regression, Random Forest, Gradient Boosting, and Support Vector Machine. Experimental results demonstrate a recognition rate of 96.32% using the Support Vector Machine classifier, with an average recall and precision of 96.22% and 96.32% respectively.