Yoga Posture Recognition and Classification Systems: A Comprehensive Review of Multi-Modal Approaches and Applications

Abstract

Purpose: This study aims to critically review hybrid yoga posture recognition systems in order to propose an integrated multi-layer hybrid framework for accurate, real-time posture assessment in healthcare and rehabilitation contexts. Design / Methodology / Approach: Yoga posture recognition methods were taxonomically analysed using a four-dimensional framework, including input modality, feature representation, learning paradigms (SVM, CNN, LSTM), and system-level integration, with multi-metric performance evaluation. Research Limitation: The proposed hybrid framework is conceptually validated but has not yet been empirically tested in a real-world clinical cohort, representing a direction for future experimental work. Findings: Hybrid deep learning systems have the best accuracy (94%- 97%), although all solutions trade off accuracy, computational cost, and practical generalizability. Practical Implication: The hybrid CNN-LSTM system developed will support real-time posture monitoring and provide corrective feedback for remote rehabilitation, fitness coaching, and edge-deployable healthcare. Social Implication: These applications help reduce healthcare inequalities, lower treatment costs, and improve quality of life for diverse populations worldwide. Originality/Value: This integrated architecture is evaluated using a comprehensive multi-metric assessment protocol for recognising yoga postures.