Introduction: The position and pose measurement of the rehabilitation robot plays a very important role in patient rehabilitation movement, and the non-contact real-time robot position and pose measurement is of great significance. Rehabilitation training is a relatively complicated process, so it is very important to detect the training process of the rehabilitation robot in real time and accuracy. The method of the deep learning has a very good effect on monitoring the rehabilitation robot state. Methods: The structure sketch and the 3D model of the 3-PRS ankle rehabilitation robot are established, and the mechanism kinematics is analyzed to obtain the relationship between the driving input - the three slider heights - and the position and pose parameters. The whole network of the position and pose measurement is composed of two stages: (1) measuring the slider heights using the CNN based on the robot image and (2) calculating the position and pose parameter using the BPNN based on the measured slider heights from the CNN. According to the characteristics of continuous variation of the slider heights, a regression CNN is proposed and established to measure the robot slider height. Based on the data calculated by using the inverse kinematics of the 3-PRS ankle rehabilitation robot, a BPNN is established to solve the forward kinematics for the position and pose. Results: The experimental results show that the regression CNN outputs the slider height and then the BPNN accurately outputs the corresponding position and pose. Eventually, the position and pose parameters are obtained from the robot image. Compared with the traditional robot position and pose measurement method, the proposed method has significant advantages. Conclusion: The proposed 3-PRS ankle rehabilitation position and pose method can not only shorten the experiment period and cost, but also get excellent timeliness and precision. The proposed approach can help the medical staff to monitor the status of the rehabilitation robot and help the patient rehabilitation in training. Discussion: The goal of the work is to construct a new position and pose detection network based on the combination of the regression convolutional neural network (CNN) and the back propagation neural network (BPNN). The main contribution is to measure the position and pose of the 3-PRS ankle rehabilitation robot in real time, which improves the measurement accuracy and the efficiency of the medical staff work.
Background:: The parallel mechanism plays an important role in various fields. The multi-functional integrated innovation experiment platform can improve the utilization rate of the mechanism and be applied in many fields. Objective:: The main objective of the study is to establish an integrated innovation experiment platform based on the 3-PRS parallel mechanism, which can be used in typical application and related technology development. Methods:: The integrated innovation experiment platform is established and analyzed based on the 3-PRS parallel mechanism. According to the 3D model of the experiment platform, the kinematics and dynamics are analyzed. The force/position control strategy of the system is adopted. According to the function of the experiment platform, two kinds of application and the position and pose measurement technology are developed. The experiment platform is developed by the following methods: (1) The XY table is set up on the fixing platform of the 3-PRS parallel mechanism, so that the mechanism has five degrees of freedom, and the many kinds of workpiece can be easily processed. (2) By selecting the impedance parameter, the experiment platform can realize the compliant control of plantar flexion/dorsiflexion and varus/eversion simultaneously. (3) The binocular vision position and pose measurement method is used to obtain the marked images of the experiment platform through dual cameras, and the position and pose is obtained through image processing, 3D reconstruction and stereo matching, etc. (4) The position and pose detection based on deep learning is divided into two parts: one is to detect the slider height using the regression Convolutional Neural Network (CNN); the other is to compute the position and pose using the Back Propagation Neural Network (BPNN). Results:: The experiment results show that the function of the 3-PRS parallel mechanism integrated innovation experiment platform can be effectively realized. The position and pose can be accurately measured in real time using the proposed two measurement methods. The impedance parameters are selected to achieve the rehabilitation training function of the 3-PRS ankle rehabilitation robot and the characters are processed to verify the function of the 3-PRS-XY series-parallel machine tool. Conclusion:: The integrated innovation experiment platform based on the 3-PRS parallel mechanism can achieve the function of mechanical processing and rehabilitation training, and can also measure the state of motion in real time through machine vision and deep learning.
Background: The study on facemask detection is of great significance because facemask detection is difficult, and the workload is heavy in places with a large number of people during the COVID-19 outbreak. Objective: The study aims to explore new deep learning networks that can accurately detect facemasks and improve the network's ability to extract multi-level features and contextual information. In addition, the proposed network effectively avoids the interference of objects like masks. The new network could eventually detect masks wearers in the crowd. Method: A Multi-stage Feature Fusion Block (MFFB) and a Detector Cascade Block (DCB) are proposed and connected to the deep learning network for facemask detection. The network's ability to obtain information improves. The network proposed in the study is Double Convolutional Neural Networks (CNN) called DCNN, which can fuse mask features and face position information. During facemask detection, the network extracts the featural information of the object and then inputs it into the data fusion layer. Results: The experiment results show that the proposed network can detect masks and faces in a complex environment and dense crowd. The detection accuracy of the network improves effectively. At the same time, the real-time performance of the detection model is excellent. Conclusion: The two branch networks of the DCNN can effectively obtain the feature and position information of facemasks. The network overcomes the disadvantage that a single CNN is susceptible to the interference of the suspected mask objects. The verification shows that the MFFB and the DCB can improve the network's ability to obtain object information, and the proposed DCNN can achieve excellent detection performance.
Background: The vehicle pose detection plays an important role in monitoring vehicle behavior and parking situation. The real-time detection of vehicle pose with high accuracy is of great importance. Objective: The goal of the work is to construct a new network to detect the vehicle angle based on the regression Convolutional Neural Network (CNN). The main contribution is that several traditional regression CNNs are combined as the Multi-Collaborative Regression CNN (MCR-CNN), which greatly enhances the vehicle angle detection precision and eliminates the abnormal detection error. Methods: Two challenges of the traditional regression CNN have been revealed in detecting the vehicle pose angle. The first challenge is the detection failure resulting from the conversion of the periodic angle to the linear angle, while the second is the big detection error if the training sample value is very small. A MCR-CNN is proposed to solve the first challenge. And a 2-stage method is proposed to solve the second challenge. The architecture of the MCR-CNN is designed in detail. After the training and testing data sets are constructed, the MCR-CNN is trained and tested for vehicle angle detection. Results: The experimental results show that the testing samples with the error below 4° account for 95% of the total testing samples based on the proposed MCR-CNN. The MCR-CNN has significant advantages over the traditional vehicle pose detection method. Conclusion: The proposed MCR-CNN can not only detect the vehicle angle in real time, but also has very high detection accuracy and robustness. The proposed approach can be used for autonomous vehicle and parking lot monitoring.
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