Towards Intelligent Yoga Posture Monitoring: A TinyML Perspective

Intelligent yoga posture monitoring has emerged as a promising application of computer vision and machine learning, offering opportunities to support posture recognition and alignment assessment during yoga practice. However, the computational demands of conventional models and their reliance on cloud-based processing can restrict deployment on resource-constrained devices. Tiny Machine Learning (TinyML) provides a potential pathway towards efficient, on-device yoga monitoring under stringent memory, computational, and energy constraints. This paper reviews existing research on yoga pose recognition, human pose estimation, lightweight neural network architectures, and embedded machine learning to examine their relevance to intelligent posture monitoring. A conceptual framework is presented that encompasses data acquisition, preprocessing, pose representation, lightweight inference, posture assessment, and feedback generation. Camera-based and wearable sensor-based approaches are discussed in terms of their applicability, advantages, and deployment limitations. Model compression techniques, particularly quantization and pruning, are examined as potential strategies for reducing computational and memory requirements. The paper further identifies key challenges involving dataset diversity, model generalization, sensor reliability, privacy, and the reliability of posture-related feedback. As a literature-based study, this work proposes a conceptual framework rather than a newly implemented or experimentally validated system. Future research should assess practical feasibility on embedded hardware using metrics such as recognition accuracy, memory footprint, inference latency, and energy consumption. This review establishes a conceptual foundation for developing efficient, privacy-aware, and accessible yoga posture monitoring systems through TinyML.