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Related Experiment Video

Updated: Jun 30, 2026

Visualizing Motion Patterns in Acupuncture Manipulation
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MetaAcuPoint: MetaHuman-Generated Synthetic Data for Hand Acupoint Localization.

Kasunika Guruge1, Prathiksha Padmanabha1, H M K K M B Herath1

  • 1Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Republic of Korea.

Healthcare (Basel, Switzerland)
|December 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces MetaAcuPoint, a synthetic dataset for precise acupoint localization in Traditional Korean Medicine. The synthetic data enables accurate, standardized acupoint identification, overcoming limitations of traditional methods.

Keywords:
MetaHumanacupoint localizationannotation consistencydeep learningsynthetic datavirtual reality

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Traditional Korean Medicine

Background:

  • Precise acupuncture point (acupoint) localization is vital for Traditional Korean Medicine (TKM) clinical success.
  • Traditional acupoint identification methods are subjective, hindering standardization.
  • Existing data-driven techniques face challenges due to limited, inconsistently labeled datasets.

Purpose of the Study:

  • To introduce MetaAcuPoint, a novel synthetic dataset for high-fidelity acupoint localization.
  • To provide anatomically consistent hand images for overcoming dataset limitations in TKM research.
  • To facilitate standardized, data-driven acupoint identification.

Main Methods:

  • Generated 900 RGB hand images using MetaHuman avatars in Unreal Engine.
  • Implemented precise, bone-attached sockets for five hand acupoints with millimeter accuracy.
  • Assessed dataset validity by training a High-Resolution Network (HRNet-W48) and testing on real-world images.

Main Results:

  • Synthetic-trained model achieved a mean distance error (MDE) of 5.67 pixels, comparable to real-data baseline (4.81 pixels).
  • Synthetic data improved performance when added to real data (MDE: 4.95 pixels).
  • Synthetic data-trained models generalized better to external datasets than real-data-trained models (MDE: 5.84-6.45 mm vs. 10.63-15.80 mm).

Conclusions:

  • MetaAcuPoint represents the first synthetic-to-real generalization for hand acupoint localization.
  • The dataset combines photorealistic rendering with anatomically grounded annotation.
  • MetaAcuPoint offers a reliable resource for advancing standardized, data-driven acupuncture research and practice.