Acupuncture points have a positive effect on the auxiliary prevention and treatment of diseases, so medical devices such as acupuncture robots often need to combine acupuncture points to improve the treatment effect when working, however, intelligent acupoint selection technology is not yet mature, the automatic rapid and accurate positioning of acupoints is still challenging. Therefore, this paper proposes a method of back acupoint location and an evaluation index of acupoint location. First, we propose an improved Keypoint RCNN network for the preliminary location of back acupoints and introduce a channel and spatial attention mechanism module (CBAM) to improve the performance of the model. Then, we set up a posterior median line positioning method to improve the accuracy of acupoint positioning. Finally, expand and locate other acupoints according to the prior information of acupoints. According to the experimental results, the accuracy of acupoint positioning was 87.32%. After the correction of acupoint positioning, the accuracy was increased by 2.8%, which was 90.12%. In this paper, the application of depth learning in automatic location of back acupoints is realized for the first time. Only one image can be used to locate the back acupoints, with an accuracy of 90.12%.
Keywords: acupoint location; evaluation method of acupoint positioning; improved keypoint RCNN; posterior median line positioning method; prior information of acupoints.
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