Cross-view Multimodal Vision-Based Assessment Framework for Traditional Chinese Medicine Rehabilitation Training
arXiv:2606.28104v1 Announce Type: cross Abstract: Vision-based assessment can provide convenient and cost-effective evaluation in Traditional Chinese Medicine (TCM) rehabilitation training, where action quality assessment (AQA) from computer vision offers a promising solution. Existing automatic AQA frameworks for physical therapy typically rely on skeletal data captured from a single viewpoint, which is inefficient for TCM techniques such as acupuncture or Tuina that involve dense hand self-occlusion and complex hand-object interactions. To address these challenges, we propose CME-AQA, a cros
The paper demonstrates current advancements in computer vision and AI for novel applications, showing continuous progress in specialized AI frameworks for real-world problems.
This development indicates practical applications of AI in healthcare, particularly in traditional medicine, offering cost-effective and convenient assessment methods.
The ability to accurately assess complex movements like those in TCM rehabilitation through AI will improve therapy efficacy and accessibility, moving beyond single-view limitations.
- · Healthcare providers
- · Patients needing rehabilitation
- · AI/Computer Vision developers
- · Traditional Chinese Medicine practitioners
- · Manual assessment methods
- · Single-view motion capture systems
Improved accuracy and accessibility of rehabilitation assessment in specific medical fields.
Expansion of AI-driven diagnostic and assessment tools into other specialized medical practices.
Potential for integration of such AI frameworks into broader digital health platforms, changing long-term care models.
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