INTELLIGENT MOVEMENT TRACKING IN PERFORMING ARTS - Ghosh, Mr. Debanjan; Kumar, Ravi; Singh, Hareram; Goel, Pooja; Somatkar, Avinash; Singh, Swarnima (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Intelligent movement tracking performing arts has become a paradigm shift in research, where computer intelligence is applied to creative performance. Conventional methods of motion analysis, most of which rely on manual observation, marker tracking systems, or single sensory modes, are incapable of tracking the subtleties, fluidity, and stylistic diversity of dances, theatre and performance. The recent innovations in artificial intelligence, multimodal sensing, and real-time analytics provide new opportunities to measure expressive movement with an unprecedented accuracy. This paper suggests an all-encompassing design that is based on optical cameras, inertial measurement units, depth sensors, and wearable devices and combines them with cutting-edge machine learning algorithms, including CNN-based pose estimators, graph convolution networks, and transformers. The system architecture has the focus of multimodal fusion, through which it is possible to consider the concurrent perception of visual, inertial, acoustic, and biomechanical signals to gain deeper insights into human movement. The processes of live performance environments, strong annotation of expressive and stylistic features, and deep learning architecture design based on the dynamics of performing arts are developed as a methodological pipeline. They have been applied to choreography analysis, automated assessment of movement-quality, intelligent systems of teaching dance and acting, and performance optimization using biomechanical feedback.