ArticleScientific reports2025
AI-Driven Tai Chi mastery using deep learning framework for movement assessment and personalized training.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effects of digitalized traditional Chinese exercises on the physical and mental health and quality of life of older adults: a systematic review and meta-analysis of randomized controlled trials.Frontiers in public health · 2025Pooled it
- Decision-centered wearable biosensors for personalized rehabilitation: integrating multimodal monitoring, artificial intelligence, and closed-loop intervention.Frontiers in bioengineering and biotechnology · 2026Review
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Authors and funding
1 author.
Funding
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Abstract
This paper presents a novel system for optimizing Tai Chi movement training using computer vision and deep learning technologies. We developed a comprehensive framework incorporating multi-view pose estimation, temporal feature extraction, and real-time movement assessment to address the challenges of traditional Tai Chi instruction. The system employs spatial-temporal graph convolutional networks enhanced with attention mechanisms for accurate movement evaluation, combined with personalized feedback generation through augmented reality and multi-modal interfaces. Validation experiments with 120 participants across different skill levels demonstrated 42% faster skill acquisition and 28.5% greater improvement in movement quality compared to traditional training methods. The system achieved 92.8% accuracy in error detection and maintained high user satisfaction ratings across all experience levels. Our approach successfully bridges ancient wisdom with modern technology, providing scalable, standardized instruction while preserving the cultural essence of Tai Chi practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.