Pointing Gesture Recognition from 3D Human Skeleton Data - Lucas, Miquelly Nicolini; Altoé, Gabriel Donna; Cosmi Filho, Luiz Carlos; Vassallo, Raquel Frizera (Anais Estendidos da Conference on Graphics, Patterns and Images (SIBGRAPI); 2025: Anais Estendidos da XXXVIII Conference on Graphics, Patterns and ImagesDO - 10.5753/sibgrapi.est.2025.38311, 2025)
Gesture recognition in Human-Machine Interaction (HMI) refers to the automatic detection of human gestures, assigning semantic meaning to physical movements and enabling interaction with computers, robots, or the analysis of human behavior. This work proposes a method for static gesture recognition, focusing specifically on the “pointing” gesture, based on 3D human skeletons reconstructed through a multi-camera system. The objective is to automatically detect the presence or absence of the gesture using spatial pose data derived from the three-dimensional reconstruction of the human body from multiple viewpoints. Following manual annotation and segmentation of the gesture sequences, structural features were extracted from the skeletons. A normalization process was applied, which performs translation to the origin and rotation to align the skeleton with the X-axis. Classical supervised machine learning models were then employed to classify body poses: Logistic Regression, Random Forest, and Decision Tree. Experiments were carried out using Leave-One-Subject-Out (LOSO) cross-validation. The results demonstrate the viability of the proposed approach for applications in intelligent environments, where recognizing the pointing gesture could be used to indicate goals, highlight objects of interest, or define target positions for mobile robots.