Acupoint therapy plays a crucial role in the prevention and treatment of various diseases.Accurate and efficient intelligent acupoint recognition methods are essential for enhancing the operational capabilities of emb...Acupoint therapy plays a crucial role in the prevention and treatment of various diseases.Accurate and efficient intelligent acupoint recognition methods are essential for enhancing the operational capabilities of embodied intelligent robots in acupoint massage and related applications.This paper proposes a lightweight hand acupoint recognition(LHAR)method based on middle finger cun measurement.First,to obtain a lightweight model for rapid positioning of the hand area,on the basis of the design of the partially convolutional gated regularisation unit and the efficient shared convolutional detection head,an improved YOLO11 algorithm based on a lightweight efficient shared convolutional detection head(YOLO11-SH)was proposed.Second,according to the theory of traditional Chinese medicine,a method of positional relationship determination between acupoints based on middle finger cun measurement is established.The MediaPipe algorithm is subsequently used to obtain 21 keypoints of the hand and serves as a reference point for obtaining features of middle finger cun via positional relationship determination.Then,the offset-based localisation approach is adopted to achieve accurate recognition of acupoints by using the obtained feature of middle finger cun.Comparative experiments with five representative lightweight models demonstrate that YOLO11-SH achieves an mAP@0.5 of 97.3%,with 1.59×10°parameters,3.9×10°FLOPs,a model weight of 3.4 MB and an inference speed of 325.8 FPS,outperforming the comparison methods in terms of both recognition accuracy and model eff-ciency.The experimental results of acupoint recognition indicate that the overall recognition accuracy of LHAR has reached 94.49%.The average normalised displacement error for different acupoints ranges from 0.036 to 0.105,all within the error threshold of≤0.15.Finally,LHAR is integrated into the robotic platform,and a robotic massage experiment is conducted to verifytheeffectiveness of LHAR.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.62373116 and 62163007)the Guizhou Provincial Science and Technology Projects,China(Grant Nos.QKHZC[2023]118,QKHZC[2023]117,PTRC-GHB[2023]001,PTRC[2020]6007-2 and GHJD[2025]005).
文摘Acupoint therapy plays a crucial role in the prevention and treatment of various diseases.Accurate and efficient intelligent acupoint recognition methods are essential for enhancing the operational capabilities of embodied intelligent robots in acupoint massage and related applications.This paper proposes a lightweight hand acupoint recognition(LHAR)method based on middle finger cun measurement.First,to obtain a lightweight model for rapid positioning of the hand area,on the basis of the design of the partially convolutional gated regularisation unit and the efficient shared convolutional detection head,an improved YOLO11 algorithm based on a lightweight efficient shared convolutional detection head(YOLO11-SH)was proposed.Second,according to the theory of traditional Chinese medicine,a method of positional relationship determination between acupoints based on middle finger cun measurement is established.The MediaPipe algorithm is subsequently used to obtain 21 keypoints of the hand and serves as a reference point for obtaining features of middle finger cun via positional relationship determination.Then,the offset-based localisation approach is adopted to achieve accurate recognition of acupoints by using the obtained feature of middle finger cun.Comparative experiments with five representative lightweight models demonstrate that YOLO11-SH achieves an mAP@0.5 of 97.3%,with 1.59×10°parameters,3.9×10°FLOPs,a model weight of 3.4 MB and an inference speed of 325.8 FPS,outperforming the comparison methods in terms of both recognition accuracy and model eff-ciency.The experimental results of acupoint recognition indicate that the overall recognition accuracy of LHAR has reached 94.49%.The average normalised displacement error for different acupoints ranges from 0.036 to 0.105,all within the error threshold of≤0.15.Finally,LHAR is integrated into the robotic platform,and a robotic massage experiment is conducted to verifytheeffectiveness of LHAR.
