TinyML-Based Yoga Pose Recognition and Posture Assessment on Resource-Constrained Devices

Yoga pose recognition and posture assessment have gained attention with advances in computer vision and machine learning. However, computationally intensive models and cloud-dependent processing can limit deployment on low-cost, resource-constrained devices. Tiny Machine Learning (TinyML) offers a promising approach by enabling on-device inference with limited memory, processing power, and energy consumption. This paper reviews existing research on yoga pose recognition, human pose estimation, lightweight neural networks, and embedded machine learning. A conceptual framework is proposed that integrates data acquisition, preprocessing, pose feature extraction, lightweight model inference, posture assessment, and feedback generation. Both camera-based and wearable sensor-based approaches are considered, highlighting their advantages and limitations for embedded deployment. Model optimization techniques, including quantization and pruning, are discussed as potential methods for reducing memory and computational requirements. The paper also examines challenges related to dataset diversity, model generalization, sensor reliability, privacy, and the accuracy of posture feedback. As this study presents a literature review and conceptual framework, no new implementation or experimental performance results are claimed. Future research should evaluate the proposed framework on physical embedded devices using recognition accuracy, memory usage, inference latency, and energy consumption. This work provides a foundation for developing efficient, privacy-conscious, and accessible yoga monitoring systems using TinyML.