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Physiotherapy robots offer a feasible and promising solution for achieving safe and efficient treatment. Among these, acupoint recognition is the core component that ensures the precision of physiotherapy robots. Although the research on the acupoint recognition such as hand and ear has been extensive, the accurate location of acupoints on the back of the human body still faces great challenges due to the lack of significant external features. This paper designs a two-stage acupoint recognition method, which is achieved through the cooperation of two detection networks. First, a lightweight RTMDet network is used to extract the effective back range from the image, and then the acupoint coordinates are inferred from the extracted back range, reducing the inference consumption caused by invalid information. In addition, the RTMPose network based on the SimCC framework converts the acupoint coordinate regression problem into a classification problem of sub-pixel block subregions on the X and Y axes by performing sub-pixel-level segmentation of images, significantly improving detection speed and accuracy. Meanwhile, the multi-layer feature fusion of CSPNeXt enhances feature extraction capabilities. Then, we designed a physiotherapy interaction interface. Through the three-dimensional coordinates of the acupoints, we independently planned the physiotherapy task path of the physiotherapy robot. We conducted performance tests on the acupoint recognition system and physiotherapy task planning in the physiotherapy robot system. The experiments have proven our effectiveness, achieving a recall of 90.17% on human datasets, with a detection error of around 5.78 mm. At the same time, it can accurately identify different back postures and achieve an inference speed of 30 FPS on a 4070Ti GPU. Finally, we conducted continuous physiotherapy tasks on multiple acupoints for the user. The experimental results demonstrate the significant advantages and broad application potential of this method in improving the accuracy and reliability of autonomous acupoint recognition by physiotherapy robots.

PurposeTo ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.Design/methodology/approachFirst, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.FindingsImproved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.Originality/valueThis paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

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 × 106 parameters, 3.9 × 109 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 efficiency. 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 verify the effectiveness of LHAR.

This paper presents the mechanical architecture, control system, and other modules of a physiotherapy service robot which can treat degenerative disease and chronic disease of middle-aged and aged people by Chinese massage skill. The main body of the robot includes a massage adjustable bed, two 4-DOF(Degree of Freedom) robot arms and two massage hands that can accomplish various massage manipulations. Physiological signal and massage pressure are detecting in real time in massage process to ensure a scientific and safe therapy. Vision system sends the recognized acupoint position to the master system to track the patient’s body, and the acupoint being massaged is displayed in real time by the 3D virtual model. The robot can execute ten massage manipulations, so that the traditional Chinese massage can have a robot instead. The effectiveness for degenerative lumbago in middle-aged and aged is demonstrated by laboratory examination and clinical trial.

This paper presents the scheme of a physiotherapy service robot including the mechanical architecture, control system, visual position system, etc. The robot can treat degenerative disease and chronic disease of middle-aged and aged people by Chinese massage skill, the main body of which includes a massage adjustable bed, two 4-DOF robot arms and two massage hands that can accomplish various massage manipulations. Two arms cooperate to improve the massage efficiency, and provide sufficient strength and enough reachable workspace for massage. The manipulators are controlled by a TRIO multi-axes motion controller and a embedded computer module. Physiological signal and massage pressure is detecting in real time in massage process to ensure a scientific and safe therapy. Vision System sends the recognized acupoint position to the master system to track the patients body, and the acupoint being massaged is displayed in real time by the 3D virtual display model. The robot can execute ten massage manipulations, which make traditional Chinese massage can have a robot instead. The effectiveness for degenerative lumbago in middle-aged and aged is demonstrated by laboratory examination and clinical trial.

Acupressure is a noninvasive method in traditional Chinese medicine to increase blood circulation, relieve discomfort, promote health, and prevent disease. This paper presents a multifunctional acupoint application system that not only provides real-time visualization of acupoints on mobile devices for users’ self-acupressure but also provides health care, education, and entertainment through acupressure. The system allows users to obtain acupoint information to relieve specific discomfort or symptoms. It automatically marks the requested acupoints on a real-time camera stream to guide users to locate acupoints so that even users unfamiliar with acupoints can easily find acupoints for self-acupressure. Three core technologies are used: acupoint localization and knowledge, real-time image recognition, and augmented reality. This paper details the system design and implementation. This system uses Google’s machine learning kit for facial detection to extract the user’s facial features and contours in real time on a mobile device. These contour points are used as landmarks with specific displacements to locate acupoints. As there are many acupoints and various positioning methods, the acupoint location is traditionally described in words through specific feature points and corresponding displacements related to each person’s physiological characteristics. This work proposes a novel acupoint positioning and marking mechanism based on the standard acupoint localization, which formalizes various acupoint positioning methods so that positions can be processed and stored in an acupoint database established in this system. This positioning information allows the proposed mechanism to fulfill real-time visualization of acupoints in augmented reality on the camera stream. Experimental results show that the proposed system locates and marks acupoints with low latency and high accuracy. Moreover, the system also provides recommendations for further medical treatment and a mini-game that tests knowledge of acupoints, which enhances users’ experience of acupoint massage.

Acupoint localization is integral to Traditional Chinese Medicine (TCM) acupuncture diagnosis and treatment. Employing intelligent detection models for recognizing facial acupoints can substantially enhance localization accuracy. This study introduces an advancement in the YOLOv8-pose keypoint detection algorithm, tailored for facial acupoints, and named YOLOv8-ACU. This model enhances acupoint feature extraction by integrating ECA attention, replaces the original neck module with a lighter Slim-neck module, and improves the loss function for GIoU. The YOLOv8-ACU model achieves impressive accuracy, with an mAP@0.5 of 97.5% and an mAP@0.5-0.95 of 76.9% on our self-constructed datasets. It also marks a reduction in model parameters by 0.44M, model size by 0.82 MB, and GFLOPs by 9.3%. With its enhanced recognition accuracy and efficiency, along with good generalization ability, YOLOv8-ACU provides significant reference value for facial acupoint localization and detection. This is particularly beneficial for Chinese medicine practitioners engaged in facial acupoint research and intelligent detection.

Asthma is a high-risk disease based on airway hyperresponsiveness (AHR). In this review, we found that there are many studies on clinical therapy for asthma that focus on the efficacy of acupuncture therapy and its mechanisms, including the functional connectivity of different brain regions, with the aid of functional magnetic resonance imaging (fMRI), immune responses/cell recognition (innate lymphoid cells and balance of Th1/Th2 and Treg/Th17), intracellular mechanism (autophagy, endoplasmic reticulum stress, and epigenetic alteration), and ligand-receptor/chemical signaling pathway (neurotransmitter, hormone, and small molecules). In this review, we summarized the clinical and experimental evidence for the mechanisms of acupuncture therapy in asthma to offer insights into drug discovery and clinical therapy. Given the paucity of clinical studies on the mechanisms of acupuncture in the treatment of asthma, this review notably included studies based on animal models to investigate the mechanisms of acupuncture in the treatment of asthma.