Human shoulder joints exhibit stable but highly active characteristics due to a large amount of soft tissues.Finite Element(FE)modelling plays an important role in enhancing our understanding of the mechanism of shoul...Human shoulder joints exhibit stable but highly active characteristics due to a large amount of soft tissues.Finite Element(FE)modelling plays an important role in enhancing our understanding of the mechanism of shoulder disorders.However,the previous FE shoulder models largely neglected the Three-Dimensional(3D)volume of soft tissues and their sophisticated interactions with the skeletons.This study develops a 3D model of the rotator cuff and deltoid muscles and tendons.It also includes cartilage and,for the first time,main ligaments around the joint to provide a better computational representation of the delicate interaction of the soft tissues.This model has potential value for studying the force transfer mechanism and overall joint stability variation caused by 3D pathological changes of rotator cuff tendons.Motion analysis systems and Magnetic Resonance(MR)scans were used to collect shoulder movement and geometric data from a young healthy subject,respectively.Based on MR images,a FE model with detailed representations of the musculoskeletal components was constructed.A multi-body model and the measured motion data were utilised to estimate the loading and boundary conditions.Quasi-static FE analyses simulated four instants of the measured scapular abduction.Simultaneously determined glenohumeral motion,stress/strain distribution in soft tissues,contact area,and mean/peak contact pressure were found to increase monotonically from 0°to 30°of abduction.The results of muscle forces,bone-on-bone contact force,and superior-inferior movement of the humeral centre during motion were consistent with previous experimental and numerical results.It is concluded that the constructed FE shoulder model can accurately estimate the biomechanics in the investigated range of motion and may be further used for the comprehensive study of shoulder musculoskeletal disorders.展开更多
As a new type of brain-computer interface(BCI),the rapid serial visual presentation(RSVP)paradigm has attracted significant attention.The mechanism of RSVP is detecting the P300 component corresponding to the target i...As a new type of brain-computer interface(BCI),the rapid serial visual presentation(RSVP)paradigm has attracted significant attention.The mechanism of RSVP is detecting the P300 component corresponding to the target image to realize fast and correct recognition.This paper proposed an improved EEGNet model to achieve good performance in offline and online data.Specifically,the data were filtered by xDAWN to enhance the signal-to-noise ratio of the electroencephalogram(EEG)signals.The focal loss function was used instead of the cross-entropy loss function to solve the classification problems of unbalanced samples.Additionally,the subject-specific data were fed to the improved EEGNet model to obtain a subject-specific model.We applied the proposed model at the BCI Controlled Robot Contest in World Robot Contest 2021 and won the second place.The average recall rate of the four participants reached 51.56%in triple classification.In the offline data benchmark dataset(64 subjects-RSVP tasks),the average recall rates of groups A and B reached 76.07%and 78.11%,respectively.We provided an alternative method to identify targets based on the RSVP paradigm.展开更多
基金This work was supported by the Grant of Bio-technology and Biological Sciences Research Council of GB(No.BB/H002782/1)the Project of National Natural Science Foundation of China(Nos.51475202 and 51675222).
文摘Human shoulder joints exhibit stable but highly active characteristics due to a large amount of soft tissues.Finite Element(FE)modelling plays an important role in enhancing our understanding of the mechanism of shoulder disorders.However,the previous FE shoulder models largely neglected the Three-Dimensional(3D)volume of soft tissues and their sophisticated interactions with the skeletons.This study develops a 3D model of the rotator cuff and deltoid muscles and tendons.It also includes cartilage and,for the first time,main ligaments around the joint to provide a better computational representation of the delicate interaction of the soft tissues.This model has potential value for studying the force transfer mechanism and overall joint stability variation caused by 3D pathological changes of rotator cuff tendons.Motion analysis systems and Magnetic Resonance(MR)scans were used to collect shoulder movement and geometric data from a young healthy subject,respectively.Based on MR images,a FE model with detailed representations of the musculoskeletal components was constructed.A multi-body model and the measured motion data were utilised to estimate the loading and boundary conditions.Quasi-static FE analyses simulated four instants of the measured scapular abduction.Simultaneously determined glenohumeral motion,stress/strain distribution in soft tissues,contact area,and mean/peak contact pressure were found to increase monotonically from 0°to 30°of abduction.The results of muscle forces,bone-on-bone contact force,and superior-inferior movement of the humeral centre during motion were consistent with previous experimental and numerical results.It is concluded that the constructed FE shoulder model can accurately estimate the biomechanics in the investigated range of motion and may be further used for the comprehensive study of shoulder musculoskeletal disorders.
基金This work is granted by the Special Projects in Key Fields Supported by the Technology Development Project of Guangdong Province(Grant No.2020ZDZX3018)the Special Fund for Science and Technology of Guangdong Province(Grant No.2020182)+2 种基金the Wuyi University and Hong Kong&Macao Joint Research Project(Grant No.2019WGALH16)the Guangdong Basic and Applied Basic Research Foundation(Grant No.2020A1515111154)the Characteristic Innovation Projects of Ordinary Universities in Guangdong Province(Grant No.2021KTSCX136).
文摘As a new type of brain-computer interface(BCI),the rapid serial visual presentation(RSVP)paradigm has attracted significant attention.The mechanism of RSVP is detecting the P300 component corresponding to the target image to realize fast and correct recognition.This paper proposed an improved EEGNet model to achieve good performance in offline and online data.Specifically,the data were filtered by xDAWN to enhance the signal-to-noise ratio of the electroencephalogram(EEG)signals.The focal loss function was used instead of the cross-entropy loss function to solve the classification problems of unbalanced samples.Additionally,the subject-specific data were fed to the improved EEGNet model to obtain a subject-specific model.We applied the proposed model at the BCI Controlled Robot Contest in World Robot Contest 2021 and won the second place.The average recall rate of the four participants reached 51.56%in triple classification.In the offline data benchmark dataset(64 subjects-RSVP tasks),the average recall rates of groups A and B reached 76.07%and 78.11%,respectively.We provided an alternative method to identify targets based on the RSVP paradigm.
