3D Facial Acupoint Localization Using Depth Camera Segmentation
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Solution Overview
Problem
Current facial acupoint locating methods are inefficient, lack accuracy, and are not suitable for real-time detection, with traditional methods relying on subjective expertise and modern methods being costly and involving radiation exposure.
Innovation Solution
A facial acupoint locating method using a depth camera to collect RGB and depth images, generating three-dimensional point cloud data, and inputting it into a trained face segmentation model to determine acupoint locations, eliminating the need for human expertise and reducing costs and radiation exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual acupoint locating method is used, then acupoints can be determined based on physician experience, but the process is time-consuming, lacks automation, and has strong individual subjectivity leading to deviations
Solution Approach 1:
The patent replaces the manual mechanical examination method with an automated optical imaging system. A depth camera captures RGB and depth images to generate three-dimensional point cloud data, which is then processed by a face segmentation model to automatically identify facial feature regions and locate acupoints, eliminating the need for manual physical examination
Solution Approach 2:
The patent creates a digital three-dimensional copy of the patient's face through point cloud data generated from depth imaging. This digital model allows for automated analysis and acupoint localization without requiring repeated manual examinations, preserving accuracy while reducing time loss
2Measurement precision
If modern imaging technology such as X-ray, MRI, or CT scans is used to locate acupoints, then acupoint locations can be determined through imaging, but the method is inconvenient, requires expensive devices, has high detection cost, cannot perform real-time detection, and involves radiation exposure
Solution Approach 1:
The patent replaces expensive, complex medical imaging devices with a relatively simple and inexpensive depth camera system. This camera captures RGB and depth images to create three-dimensional point cloud data, achieving acupoint localization accuracy without requiring costly equipment like MRI or CT scanners
Solution Approach 2:
The patent extracts only the necessary depth information from the imaging process by using a depth camera that specifically captures depth data along with RGB images. This selective extraction of depth information enables three-dimensional reconstruction and acupoint localization without the need for complex full-body imaging systems, reducing device complexity while maintaining precision
3Measurement precision
If traditional manual acupoint locating method is used, then acupoints can be identified, but real-time detection cannot be performed
Solution Approach 1:
The patent enables continuous real-time acupoint detection by capturing a series of depth images and RGB images sequentially. The system continuously generates updated three-dimensional point cloud data and re-processes it through the face segmentation model, allowing for real-time tracking and localization of acupoints as the patient moves or as conditions change
Data Source
AI summary
The present invention provides a facial acupoint locating method, an acupuncture method, an acupuncture robot, and a storage medium. The facial acupoint locating method includes: collecting an RGB image and a depth image of a face by using a depth camera, and generating three-dimensional point cloud data of the face based on the RGB image and the depth image; inputting the three-dimensional point cloud data of the face into a trained face segmentation model to obtain a plurality of facial feature regions, where the plurality of the facial feature regions include eyebrow regions, eye regions, a nose region, and a mouth region; and acquiring an association relationship between the plurality of the facial feature regions and key acupoint points, and determining locations of the facial acupoints based on the association relationship and the plurality of the facial feature regions.