Hand Acupoint Detection from Images Based on Improved HRNet

As an important component of Traditional Chinese Medicine (TCM), the acupoint therapy has achieved significant success in clinical practice. However, at present, the effect of acupoint therapy heavily depends on the skills of doctors and the acupuncture medical resources are seriously insufficient. The introduction of artificial intelligence technology in acupoint therapy can reduce the workload of doctors and ensure the consistency of acupoint operations, which is of great significance. The key process of acupoint therapy is acupoint detection. In this paper, we apply the deep learning method in automatic acupoint detection using images and propose an improved High-Resolution Network (HRNet) method for hand acupoint detection. What's more, we build a hand acupoint detection dataset and propose an evaluation metric. Experiments on the proposed dataset verify the effectiveness of the proposed method.