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
Tremor is considered as the most common faced abnormal involuntary movement disorder and the source of functional disability. Parkinson disease (PD) is a slowly progressive degenerative disorder of the central nervous...Tremor is considered as the most common faced abnormal involuntary movement disorder and the source of functional disability. Parkinson disease (PD) is a slowly progressive degenerative disorder of the central nervous system caused by the lack in the level of dopamine. Levodopa is the most effective dopaminergic medication used to manage Parkinson symptoms. However, it will be the source of the motor fluctuation after several years. An uncommon type of medication is suggested to suppress the resting tremor of PD patients. In this paper, a vibration absorber is used as a mechanical treatment and designed to reduce critical angular displacement amplitude at the resonance frequency. Human hand is modeled dynamically at the musculoskeletal level to reflect Parkinsonism. Motion is considered due to shoulder, elbow, Biceps brachii and wrist muscles activation. Absorber’s geometry, materials properties and parameters are well chosen to satisfy the tuning condition. The solution to the equation of motion for the hand is shown in the frequency and time domains to check the performance of the absorber in reducing the flexion angular motion at the wrist joint. Results show that the absorber was very effective over a good frequency bandwidth. It was able to reduce 93% of tremors amplitude at the wrist joint in the frequency domain. This type of absorber has low cost, can operate without power requirements, and has a simple design. Since its effectiveness was proved when tested numerically, it is recommended to proceed to the manufacturing process and the experimental study.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.
文摘Tremor is considered as the most common faced abnormal involuntary movement disorder and the source of functional disability. Parkinson disease (PD) is a slowly progressive degenerative disorder of the central nervous system caused by the lack in the level of dopamine. Levodopa is the most effective dopaminergic medication used to manage Parkinson symptoms. However, it will be the source of the motor fluctuation after several years. An uncommon type of medication is suggested to suppress the resting tremor of PD patients. In this paper, a vibration absorber is used as a mechanical treatment and designed to reduce critical angular displacement amplitude at the resonance frequency. Human hand is modeled dynamically at the musculoskeletal level to reflect Parkinsonism. Motion is considered due to shoulder, elbow, Biceps brachii and wrist muscles activation. Absorber’s geometry, materials properties and parameters are well chosen to satisfy the tuning condition. The solution to the equation of motion for the hand is shown in the frequency and time domains to check the performance of the absorber in reducing the flexion angular motion at the wrist joint. Results show that the absorber was very effective over a good frequency bandwidth. It was able to reduce 93% of tremors amplitude at the wrist joint in the frequency domain. This type of absorber has low cost, can operate without power requirements, and has a simple design. Since its effectiveness was proved when tested numerically, it is recommended to proceed to the manufacturing process and the experimental study.
