Role of smart yoga mats in postural monitoring and correction through advanced pressure-sensing mechanisms: A comprehensive review

Background: Smart yoga mats combine pressure-sensing arrays with artificial intelligence to monitor and correct posture during practice, offering potential benefits for alignment, injury prevention, and at-home guidance. Objective: This review synthesises current evidence on pressure-based smart yoga mats, examining sensor technologies, machine learning methods for posture analysis, validation results, user experience, and ethical and practical challenges, and proposes directions for future research. Methods: A narrative review was conducted using PubMed, IEEE Xplore, Scopus, and Google Scholar through 2025, supplemented by patents and manufacturer documentation. Inclusion criteria targeted systems using pressure-sensing arrays for pose recognition or real-time feedback. Extracted data included sensor type and resolution, signal processing pipelines, machine learning model architecture, feedback modalities, validation protocols, sample characteristics, and reported outcomes related to posture correction and user engagement. Results: Pressure technologies resistive, capacitive, and piezoresistive—implemented as spatial arrays capture contact maps and weight distribution crucial for alignment assessment. Machine learning approaches, notably support vector machines, random forests, and convolutional neural networks, are used for pose classification and deviation detection. Additional technical limitations include sensor calibration drift, variability across body types, power management, and data security concerns. Emerging strategies to address these issues include multimodal sensor fusion (IMUs + pressure), standardised validation frameworks, and AR/VR-enhanced guidance. Conclusion: Pressure-based smart yoga mats show promising capability for real-time posture monitoring and corrective guidance. However, broader clinical and consumer adoption requires standardised validation, long-term studies, inclusive design, privacy protections, and integration with multimodal sensing to enhance robustness. Future work should emphasize accessibility and scalability.