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Hualong Xie,Xiaofei Zhao,Qiancheng Sun,Kun Yang,Fei Li 대한기계학회 2020 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.34 No.1
Our research team combined humanoid robots with intelligent lower limb prostheses to study the dynamic characteristics of intelligent lower extremity prostheses for disabled people in the walking process, and proposed a biped robot with heterogeneous legs (BRHL). This paper proposes a new virtual-real inverted pendulum system model to unify the models for both single support phase and double support phase in walking process and builds a special simulation platform which can acquire the real-time center of mass (COM) trajectory. Initially, a gravity-compensated inverted pendulum model was built and improved the stability of gait, a natural ZMP trajectory improved the anthropomorphism of the gait. Furthermore, in double support phase, a virtual inverted pendulum model was established and a virtual-real inverted pendulum model was proposed and used to plan the gait of both single support phase and double support phase in the walking process. Additionally, the joint angles were obtained by inverse kinematics; the stability of the system was analyzed to be feasible and effective by phase trajectories. A special ADAMS simulation platform was built to simulate the walking process and acquire real-time COM trajectory. The feasibility of the gait planning was also verified. Finally, the trajectory of COM was optimized based on the minimum energy criterion according to the geodesic equation.
Lu Yi,Wu Jiachuan,Hu Minhui,Zhong Qinghua,Er Limian,Shi Huihui,Cheng Weihui,Chen Ke,Liu Yuan,Qiu Bingfeng,Xu Qiancheng,Lai Guangshun,Wang Yufeng,Luo Yuxuan,Mu Jinbao,Zhang Wenjie,Zhi Min,Sun Jiachen 거트앤리버 소화기연관학회협의회 2023 Gut and Liver Vol.17 No.6
Background/Aims: The accuracy of endosonographers in diagnosing gastric subepithelial lesions (SELs) using endoscopic ultrasonography (EUS) is influenced by experience and subjectivity. Artificial intelligence (AI) has achieved remarkable development in this field. This study aimed to develop an AI-based EUS diagnostic model for the diagnosis of SELs, and evaluated its efficacy with external validation. Methods: We developed the EUS-AI model with ResNeSt50 using EUS images from two hospitals to predict the histopathology of the gastric SELs originating from muscularis propria. The diagnostic performance of the model was also validated using EUS images obtained from four other hospitals. Results: A total of 2,057 images from 367 patients (375 SELs) were chosen to build the models, and 914 images from 106 patients (108 SELs) were chosen for external validation. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the model for differentiating gastrointestinal stromal tumors (GISTs) and non-GISTs in the external validation sets by images were 82.01%, 68.22%, 86.77%, 59.86%, and 78.12%, respectively. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy in the external validation set by tumors were 83.75%, 71.43%, 89.33%, 60.61%, and 80.56%, respectively. The EUS-AI model showed better performance (especially specificity) than some endosonographers. The model helped improve the sensitivity, specificity, and accuracy of certain endosonographers. Conclusions: We developed an EUS-AI model to classify gastric SELs originating from muscularis propria into GISTs and non-GISTs with good accuracy. The model may help improve the diagnostic performance of endosonographers. Further work is required to develop a multi-modal EUS-AI system.