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RISS 인기검색어
- 검색키워드
- 저자 : Xiao Yanhe (검색결과 2 건)
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Design and evaluation of a 7-DOF cable-driven upper limb exoskeleton
Feiyun Xiao,Yongsheng Gao,Yong Wang,Yanhe Zhu,Jie Zhao 대한기계학회 2018 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.32 No.2
This paper presents a seven degrees of freedom cable-driven upper limb exoskeleton (CABXLexo-7), which is compact, lightweight, and comfortable for post-stroke patients. To achieve the compactness of exoskeleton, two types of cable-driven differential mechanisms were designed. The cable-conduit mechanisms were applied to transmit the power of motors mounted on the backboard to the corresponding joints, then the whole weight of the exoskeleton could be light to ensure a comfortable motion assistance. In the course of experiments, the surface electromyography signals of major muscles related with the movements of upper limb were collected to evaluate the assistant ability of exoskeleton. The experimental results showed that the activation levels of corresponding muscles were reduced by using the seven degrees of freedom cable-driven upper limb exoskeleton in the course of rehabilitation, and it demonstrated that the exoskeleton can provide effective movements assistance to the post-stroke patients.
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Purkayastha Subhanik,Xiao Yanhe,Jiao Zhicheng,Thepumnoeysuk Rujapa,Halsey Kasey,Wu Jing,Tran Thi My Linh,Hsieh Ben,Choi Ji Whae,Wang Dongcui,Vallières Martin,Wang Robin,Collins Scott,Feng Xue,Feldman 대한영상의학회 2021 Korean Journal of Radiology Vol.22 No.7
Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.
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