Jianghan University

Qian Tu

Qiang He

Hongxing Zhang

Jing Wang

Yi Zheng

Lixin Yu

Yifan Deng

This invention provides a foot reflex zones identification method, device, rehabilitation robot, and storage medium. The method comprises the following steps: inputting a foot image into a multi-task deep learning network model, and obtaining foot part reflex zones and foot organ reflex zones; the foot part reflex zones include a head reflex zone, a neck reflex zone, a shoulder-back reflex zone, a chest reflex zone, an abdominal reflex zone, and a genital reflex zone; judging whether the foot organ reflex zones and the foot part reflex zones satisfy a theoretical position relationship, if the relationship is satisfied, determining the foot organ reflex zones as target foot organ reflex zones; wherein, the theoretical position relationship is that the foot organ reflex zones are sub-regions of each of the foot part reflex zones. This invention improves the accuracy of the identified target foot organ reflex zones.

Foot reflex zones identification method, device, rehabilitation robot, and storage medium

A method and apparatus for locating head side acupuncture points, a moxibustion robot, and a storage medium are provided. The method includes: training an acupuncture point identification model based on a head side acupuncture point sample set to obtain a target acupuncture point identification model, where the head side acupuncture point sample set includes a plurality of acupuncture point sample points and a plurality of support sample points in a curve of gallbladder meridian of foot-shaoyang; inputting a head side image to be identified to the target acupuncture point identification model to obtain a plurality of acupuncture points and a plurality of support points; fitting the plurality of acupuncture points and the plurality of support points to obtain a fitted curve; and determining whether the acupuncture points require correction based on the fitted curve, if yes, correcting the acupuncture points based on the fitted curve to obtain target acupuncture points.

Method and apparatus for locating head side acupuncture points, moxibustion robot, and storage medium

A method and apparatus for identifying back acupuncture points, and a moxibustion robot are provided, which belong to the technical field of identification of acupuncture points. The method includes: acquiring skeleton key feature points of a human body image, and identifying a plurality of back meridians on a back based on the skeleton key feature points; identifying a plurality of thoracic vertebra positions and a plurality of lumbar vertebra positions of the human body image based on the skeleton key feature points; and identifying back acupuncture points based on the plurality of thoracic vertebra positions, the plurality of lumbar vertebra positions, and the back meridians. Manual marking is not required, and the thoracic vertebra positions and the lumbar vertebra positions are used as references for the back acupuncture points. The accuracies of the identified back acupuncture points are improved.

Method and apparatus for identifying back acupuncture points, and moxibustion robot

A hand acupoint positioning method, device, acupuncture robot and storage medium, which comprises: obtaining reflex zones of a hand image and topology relationship of hand acupoints; splicing the reflex zones, the topology relationship and the hand image to obtain a hand splicing image; inputing the hand splicing image into an acupoint recognition model to obtain the hand acupoints. The invention uses reflex zones as auxiliary information and topology relationship as priority knowledge. More context information can be added to the hand acupoint positioning process, which improves the positioning accuracy of the hand acupoints.

Hand acupoint positioning method, device, acupuncture robot and storage medium

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.

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Wuhan City, China

Qian Tu