Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, f...Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.展开更多
Tenosynovitis represents a common clinical condition characterized by inflam-mation of the synovium that encases the tendon sheath.Although tenosynovities may be noted in any tendon in the body,extremities such as han...Tenosynovitis represents a common clinical condition characterized by inflam-mation of the synovium that encases the tendon sheath.Although tenosynovities may be noted in any tendon in the body,extremities such as hand,and foot remain the sites of high predilection to acquire this condition.The predominant cause of this predilection rests in the intricate tendon arrangements in these extremities that permit fine motor actions.This editorial explores the common causes and the complications associated with this condition to improve the understanding of the readers of this common condition encountered in our everyday clinical practice.展开更多
Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japane...Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for communication.However,existing JSL recognition systems have faced significant performance limitations due to inherent complexities.In response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning features.Our system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL gestures.Simultaneously,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second stream.Then,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL gestures.After reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the classification.To assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)dataset.Our results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.展开更多
In the digital age,non-touch communication technologies are reshaping human-device interactions and raising security concerns.A major challenge in current technology is the misinterpretation of gestures by sensors and...In the digital age,non-touch communication technologies are reshaping human-device interactions and raising security concerns.A major challenge in current technology is the misinterpretation of gestures by sensors and cameras,often caused by environmental factors.This issue has spurred the need for advanced data processing methods to achieve more accurate gesture recognition and predictions.Our study presents a novel virtual keyboard allowing character input via distinct hand gestures,focusing on two key aspects:hand gesture recognition and character input mechanisms.We developed a novel model with LSTM and fully connected layers for enhanced sequential data processing and hand gesture recognition.We also integrated CNN,max-pooling,and dropout layers for improved spatial feature extraction.This model architecture processes both temporal and spatial aspects of hand gestures,using LSTM to extract complex patterns from frame sequences for a comprehensive understanding of input data.Our unique dataset,essential for training the model,includes 1,662 landmarks from dynamic hand gestures,33 postures,and 468 face landmarks,all captured in real-time using advanced pose estimation.The model demonstrated high accuracy,achieving 98.52%in hand gesture recognition and over 97%in character input across different scenarios.Its excellent performance in real-time testing underlines its practicality and effectiveness,marking a significant advancement in enhancing human-device interactions in the digital age.展开更多
BACKGROUND Increasing data indicated that long noncoding RNAs(lncRNAs)were directly or indirectly involved in the occurrence and development of tumors,including hepatocellular carcinoma(HCC).Recent studies had found t...BACKGROUND Increasing data indicated that long noncoding RNAs(lncRNAs)were directly or indirectly involved in the occurrence and development of tumors,including hepatocellular carcinoma(HCC).Recent studies had found that the expression of lncRNA HAND2-AS1 was downregulated in HCC tissues,but its role in HCC progression is unclear.Ultrasound targeted microbubble destruction mediated gene transfection is a new method to overexpress genes.AIM To study the role of ultrasound microbubbles(UTMBs)mediated HAND2-AS1 in the progression of HCC,in order to provide a new reference for the treatment of HCC.METHODS In vitro,we transfected HAND2-AS1 siRNA into HepG2 cells by UTMBs,and detected cell proliferation,apoptosis,invasion and epithelial-mesenchymal transition(EMT)by cell counting kit-8 assay,flow cytometry,Transwell invasion assay and Western blotting,respectively.In addition,we transfected miR-837-5p mimic into UTMBs treated cells and observed the changes of cell behavior.Next,the UTMBs treated HepG2 cells were transfected together with miR-837-5p mimic and tissue inhibitor of matrix metalloproteinase-2(TIMP2)overexpression vector,and we detected cell proliferation,apoptosis,invasion and EMT.In vivo,we established a mouse model of subcutaneous transplantation of HepG2 cells and observed the effect of HAND2-AS1 silencing on tumor formation ability.RESULTS We found that UTMBs carrying HAND2-AS1 restricted cell proliferation,invasion,and EMT,encouraged apoptosis,and HAND2-AS1 silencing eliminated the effect of UTMBs.Additionally,miR-873-5p targets the gene HAND2-AS1,which also targets the 3’UTR of TIMP2.And miR-873-5p mimic counteracted the impact of HAND2-AS1.Further,miR-873-5p mimic solely or in combination with pcDNA-TIMP2 had been transformed into HepG2 cells exposed to UTMBs.We discovered that TIMP2 reversed the effect of miR-873-5p mimic caused by the blocked signalling cascade for matrix metalloproteinase(MMP)2/MMP9.In vivo results showed that HAND2-AS1 silencing significantly inhibited tumor formation in mice.CONCLUSION LncRNA HAND2-AS1 promotes TIMP2 expression by targeting miR-873-5p to inhibit HepG2 cell growth and delay HCC progression.展开更多
Objective:To study the application effect of the plan-do-check-act(PDCA)cycle management in the hand hygiene management of psychiatric medical staff.Methods:One hundred and twenty medical staff from a psychiatric hosp...Objective:To study the application effect of the plan-do-check-act(PDCA)cycle management in the hand hygiene management of psychiatric medical staff.Methods:One hundred and twenty medical staff from a psychiatric hospital from May 2023 to December 2023 were selected and divided into two groups.The control group(May 2023 to August 2023)applied the conventional management model,and the observation group(September 2023 to December 2023)applied the PDCA cycle management.The hand hygiene compliance,hand hygiene knowledge,and hygiene qualifications were compared,including the amount of hand sanitizer used.Results:The proportion of medical staff’s hand hygiene compliance and hand hygiene knowledge mastery scores in the observation group were higher than those in the control group(P<0.05);the hand hygiene passing rate in the observation group was higher than that of the control group(P<0.05);the daily amount of hand sanitizer per patient bed and the amount of hand sanitizer used was higher than that of the control group(P<0.05).Conclusion:The PDCA cycle management model for psychiatric medical staff promoted the improvement of hand hygiene compliance and increased their hand hygiene qualifications.It is suitable for further popularization and application in future clinical practice.展开更多
It has been proved by the ancient and modemacupuncture practices that needling techniques are ofvital importance in the acupuncture treatment ofdiseases.At present,much has been reported aboutthe researches on acupunc...It has been proved by the ancient and modemacupuncture practices that needling techniques are ofvital importance in the acupuncture treatment ofdiseases.At present,much has been reported aboutthe researches on acupuncture techniques,which canbe roughly divided into the following three types:reinforcing,reducing,and even methods.Throughlong years of study and clinical practice,Dr.展开更多
Teleoperation can assist people to complete various complex tasks in inaccessible or high-risk environments,in which a wearable hand exoskeleton is one of the key devices.Adequate adaptability would be available to en...Teleoperation can assist people to complete various complex tasks in inaccessible or high-risk environments,in which a wearable hand exoskeleton is one of the key devices.Adequate adaptability would be available to enable the master hand exoskeleton to capture the motion of human fingers and reproduce the contact force between the slave hand and its object.This paper presents a novel finger exoskeleton based on the cascading four-link closed-loop kinematic chain.Each finger has an independent closed-loop kinematic chain,and the angle sensors are used to obtain the finger motion including the flexion/extension and the adduction/abduction.The cable tension is changed by the servo motor to transmit the contact force to the fingers in real time.Based on the finger exoskeleton,an adaptive hand exoskeleton is consequently developed.In addition,the hand exoskeleton is tested in a master-slave system.The experiment results show that the adaptive hand exoskeleton can be worn without any mechanical constraints,and the slave hand can follow the motions of each human finger.The accuracy and the real-time capability of the force reproduction are validated.The proposed adaptive hand exoskeleton can be employed as the master hand to remotely control the humanoid five-fingered dexterous slave hand,thus,enabling the teleoperation system to complete complex dexterous manipulation tasks.展开更多
In this article,to reduce the complexity and improve the generalization ability of current gesture recognition systems,we propose a novel SE-CNN attention architecture for sEMG-based hand gesture recognition.The propo...In this article,to reduce the complexity and improve the generalization ability of current gesture recognition systems,we propose a novel SE-CNN attention architecture for sEMG-based hand gesture recognition.The proposed algorithm introduces a temporal squeeze-and-excite block into a simple CNN architecture and then utilizes it to recalibrate the weights of the feature outputs from the convolutional layer.By enhancing important features while suppressing useless ones,the model realizes gesture recognition efficiently.The last procedure of the proposed algorithm is utilizing a simple attention mechanism to enhance the learned representations of sEMG signals to performmulti-channel sEMG-based gesture recognition tasks.To evaluate the effectiveness and accuracy of the proposed algorithm,we conduct experiments involving multi-gesture datasets Ninapro DB4 and Ninapro DB5 for both inter-session validation and subject-wise cross-validation.After a series of comparisons with the previous models,the proposed algorithm effectively increases the robustness with improved gesture recognition performance and generalization ability.展开更多
针对采摘机器人自主行走导航过程中,难以准确定位其与果树之间的相对位置,难以准确估计果树树干姿态的问题,提出基于双目eye in hand系统的多角度树干位姿估计方法。利用YOLOv5深度学习方法与半全局块匹配算法识别树干并生成局部点云;...针对采摘机器人自主行走导航过程中,难以准确定位其与果树之间的相对位置,难以准确估计果树树干姿态的问题,提出基于双目eye in hand系统的多角度树干位姿估计方法。利用YOLOv5深度学习方法与半全局块匹配算法识别树干并生成局部点云;利用半径滤波和体素滤波减少树干点云数据;利用闭环式手眼标定方法对双目eye in hand系统进行标定,并对同一树干多角度相机位置的点云数据进行拼接;利用随机抽样一致(RANSAC)算法与无约束最小二乘法估计并优化树干的位置和姿态,获取树干的圆柱体参数。通过对30幅标定板图像进行实验,闭环式手眼标定方法的平均欧式误差为3.7177 mm;采用半径滤波和体素滤波可减少98.470%的点云数据;采用RANSAC算法、圆柱体估计算法拟合树干点云数据,得到圆柱体的半径r=41.2771 mm,R_(MAE)=2.57156 mm,R_(RMSE)=2.98936 mm;无约束最小二乘法优化后r=39.4028 mm,R_(MAE)=1.98955 mm,R_(RMSE)=2.46588 mm。该文通过对双目eye in hand系统进行标定,建立坐标系转换关系,多角度采集环境信息,准确定位机器人与果树之间的相对位置,估计果树树干的姿态。展开更多
The first major outbreak of the severely complicated hand,foot and mouth disease(HFMD),primarily caused by enterovirus 71,was reported in Taiwan in 1998.HFMD surveillance is needed to assess the spread of HFMD.The par...The first major outbreak of the severely complicated hand,foot and mouth disease(HFMD),primarily caused by enterovirus 71,was reported in Taiwan in 1998.HFMD surveillance is needed to assess the spread of HFMD.The parameters we use in mathematical models are usually classical mathematical parameters,called crisp parameters,which are taken for granted.But any biological or physical phenomenon is best explained by uncertainty.To represent a realistic situation in any mathematical model,fuzzy parameters can be very useful.Many articles have been published on how to control and prevent HFMD from the perspective of public health and statistical modeling.However,few works use fuzzy theory in building models to simulateHFMDdynamics.In this context,we examined anHFMD model with fuzzy parameters.A Non Standard Finite Difference(NSFD)scheme is developed to solve the model.The developed technique retains essential properties such as positivity and dynamic consistency.Numerical simulations are presented to support the analytical results.The convergence and consistency of the proposed method are also discussed.The proposed method converges unconditionally while the many classical methods in the literature do not possess this property.In this regard,our proposed method can be considered as a reliable tool for studying the dynamics of HFMD.展开更多
Appearance-based dynamic Hand Gesture Recognition(HGR)remains a prominent area of research in Human-Computer Interaction(HCI).Numerous environmental and computational constraints limit its real-time deployment.In addi...Appearance-based dynamic Hand Gesture Recognition(HGR)remains a prominent area of research in Human-Computer Interaction(HCI).Numerous environmental and computational constraints limit its real-time deployment.In addition,the performance of a model decreases as the subject’s distance from the camera increases.This study proposes a 3D separable Convolutional Neural Network(CNN),considering the model’s computa-tional complexity and recognition accuracy.The 20BN-Jester dataset was used to train the model for six gesture classes.After achieving the best offline recognition accuracy of 94.39%,the model was deployed in real-time while considering the subject’s attention,the instant of performing a gesture,and the subject’s distance from the camera.Despite being discussed in numerous research articles,the distance factor remains unresolved in real-time deployment,which leads to degraded recognition results.In the proposed approach,the distance calculation substantially improves the classification performance by reducing the impact of the subject’s distance from the camera.Additionally,the capability of feature extraction,degree of relevance,and statistical significance of the proposed model against other state-of-the-art models were validated using t-distributed Stochastic Neighbor Embedding(t-SNE),Mathew’s Correlation Coefficient(MCC),and the McNemar test,respectively.We observed that the proposed model exhibits state-of-the-art outcomes and a comparatively high significance level.展开更多
The severity of septic arthritis of the hand and the prospects for restoration of joint function are determined by a complex of factors. Among them, the leading role belongs to local changes in tissue structures. This...The severity of septic arthritis of the hand and the prospects for restoration of joint function are determined by a complex of factors. Among them, the leading role belongs to local changes in tissue structures. This includes the destruction of articular cartilage and bone tissue with the development of osteomyelitis, the involvement of paraarticular soft tissues in the purulent process, and the destruction of the flexor/extensor tendons of the fingers. The currently missing specialized classification of septic arthritis could help in systematizing the diseases, determining treatment tactics, and predicting the results of treatment.The classification of septic arthritis of the hand proposed for discussion is based on the following principle: Joint-Wound-Tendon(Jx Wx Tx);Jx characterizes damage to the osteochondral structures of the joint, Wx is the presence of paraarticular purulent wounds or fistulas, and Tx is destruction of the flexor/extensor tendons of the finger. The classification of the diagnosis makes it possible to assess the nature and severity of damage to the structures of the joint and may also be useful when comparing the results of treatment of septic arthritis of the hand.展开更多
Human hand detection in uncontrolled environments is a challenging visual recognition task due to numerous variations of hand poses and background image clutter.To achieve highly accurate results as well as provide re...Human hand detection in uncontrolled environments is a challenging visual recognition task due to numerous variations of hand poses and background image clutter.To achieve highly accurate results as well as provide real-time execution,we proposed a deep transfer learning approach over the state-of-the-art deep learning object detector.Our method,denoted as YOLOHANDS,is built on top of the You Only Look Once(YOLO)deep learning architecture,which is modified to adapt to the single class hand detection task.The model transfer is performed by modifying the higher convolutional layers including the last fully connected layer,while initializing lower non-modified layers with the generic pre-trained weights.To address robustness issues,we introduced a comprehensive augmentation procedure over the training image dataset,specifically adapted for the hand detection problem.Experimental evaluation of the proposed method,which is performed on a challenging public dataset,has demonstrated highly accurate results,comparable to the state-of-the-art methods.展开更多
Hand Gesture Recognition(HGR)is a promising research area with an extensive range of applications,such as surgery,video game techniques,and sign language translation,where sign language is a complicated structured for...Hand Gesture Recognition(HGR)is a promising research area with an extensive range of applications,such as surgery,video game techniques,and sign language translation,where sign language is a complicated structured form of hand gestures.The fundamental building blocks of structured expressions in sign language are the arrangement of the fingers,the orientation of the hand,and the hand’s position concerning the body.The importance of HGR has increased due to the increasing number of touchless applications and the rapid growth of the hearing-impaired population.Therefore,real-time HGR is one of the most effective interaction methods between computers and humans.Developing a user-free interface with good recognition performance should be the goal of real-time HGR systems.Nowadays,Convolutional Neural Network(CNN)shows great recognition rates for different image-level classification tasks.It is challenging to train deep CNN networks like VGG-16,VGG-19,Inception-v3,and Efficientnet-B0 from scratch because only some significant labeled image datasets are available for static hand gesture images.However,an efficient and robust hand gesture recognition system of sign language employing finetuned Inception-v3 and Efficientnet-Bo network is proposed to identify hand gestures using a comparative small HGR dataset.Experiments show that Inception-v3 achieved 90%accuracy and 0.93%precision,0.91%recall,and 0.90%f1-score,respectively,while EfficientNet-B0 achieved 99%accuracy and 0.98%,0.97%,0.98%,precision,recall,and f1-score respectively.展开更多
基金the Competitive Research Fund of the University of Aizu,Japan.
文摘Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security.
文摘Tenosynovitis represents a common clinical condition characterized by inflam-mation of the synovium that encases the tendon sheath.Although tenosynovities may be noted in any tendon in the body,extremities such as hand,and foot remain the sites of high predilection to acquire this condition.The predominant cause of this predilection rests in the intricate tendon arrangements in these extremities that permit fine motor actions.This editorial explores the common causes and the complications associated with this condition to improve the understanding of the readers of this common condition encountered in our everyday clinical practice.
基金supported by the Competitive Research Fund of the University of Aizu,Japan.
文摘Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for communication.However,existing JSL recognition systems have faced significant performance limitations due to inherent complexities.In response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning features.Our system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL gestures.Simultaneously,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second stream.Then,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL gestures.After reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the classification.To assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)dataset.Our results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.
文摘In the digital age,non-touch communication technologies are reshaping human-device interactions and raising security concerns.A major challenge in current technology is the misinterpretation of gestures by sensors and cameras,often caused by environmental factors.This issue has spurred the need for advanced data processing methods to achieve more accurate gesture recognition and predictions.Our study presents a novel virtual keyboard allowing character input via distinct hand gestures,focusing on two key aspects:hand gesture recognition and character input mechanisms.We developed a novel model with LSTM and fully connected layers for enhanced sequential data processing and hand gesture recognition.We also integrated CNN,max-pooling,and dropout layers for improved spatial feature extraction.This model architecture processes both temporal and spatial aspects of hand gestures,using LSTM to extract complex patterns from frame sequences for a comprehensive understanding of input data.Our unique dataset,essential for training the model,includes 1,662 landmarks from dynamic hand gestures,33 postures,and 468 face landmarks,all captured in real-time using advanced pose estimation.The model demonstrated high accuracy,achieving 98.52%in hand gesture recognition and over 97%in character input across different scenarios.Its excellent performance in real-time testing underlines its practicality and effectiveness,marking a significant advancement in enhancing human-device interactions in the digital age.
文摘BACKGROUND Increasing data indicated that long noncoding RNAs(lncRNAs)were directly or indirectly involved in the occurrence and development of tumors,including hepatocellular carcinoma(HCC).Recent studies had found that the expression of lncRNA HAND2-AS1 was downregulated in HCC tissues,but its role in HCC progression is unclear.Ultrasound targeted microbubble destruction mediated gene transfection is a new method to overexpress genes.AIM To study the role of ultrasound microbubbles(UTMBs)mediated HAND2-AS1 in the progression of HCC,in order to provide a new reference for the treatment of HCC.METHODS In vitro,we transfected HAND2-AS1 siRNA into HepG2 cells by UTMBs,and detected cell proliferation,apoptosis,invasion and epithelial-mesenchymal transition(EMT)by cell counting kit-8 assay,flow cytometry,Transwell invasion assay and Western blotting,respectively.In addition,we transfected miR-837-5p mimic into UTMBs treated cells and observed the changes of cell behavior.Next,the UTMBs treated HepG2 cells were transfected together with miR-837-5p mimic and tissue inhibitor of matrix metalloproteinase-2(TIMP2)overexpression vector,and we detected cell proliferation,apoptosis,invasion and EMT.In vivo,we established a mouse model of subcutaneous transplantation of HepG2 cells and observed the effect of HAND2-AS1 silencing on tumor formation ability.RESULTS We found that UTMBs carrying HAND2-AS1 restricted cell proliferation,invasion,and EMT,encouraged apoptosis,and HAND2-AS1 silencing eliminated the effect of UTMBs.Additionally,miR-873-5p targets the gene HAND2-AS1,which also targets the 3’UTR of TIMP2.And miR-873-5p mimic counteracted the impact of HAND2-AS1.Further,miR-873-5p mimic solely or in combination with pcDNA-TIMP2 had been transformed into HepG2 cells exposed to UTMBs.We discovered that TIMP2 reversed the effect of miR-873-5p mimic caused by the blocked signalling cascade for matrix metalloproteinase(MMP)2/MMP9.In vivo results showed that HAND2-AS1 silencing significantly inhibited tumor formation in mice.CONCLUSION LncRNA HAND2-AS1 promotes TIMP2 expression by targeting miR-873-5p to inhibit HepG2 cell growth and delay HCC progression.
基金2023 Guangzhou Kangning Hospital Hospital-Level Scientific Research Project(KN2023-008)。
文摘Objective:To study the application effect of the plan-do-check-act(PDCA)cycle management in the hand hygiene management of psychiatric medical staff.Methods:One hundred and twenty medical staff from a psychiatric hospital from May 2023 to December 2023 were selected and divided into two groups.The control group(May 2023 to August 2023)applied the conventional management model,and the observation group(September 2023 to December 2023)applied the PDCA cycle management.The hand hygiene compliance,hand hygiene knowledge,and hygiene qualifications were compared,including the amount of hand sanitizer used.Results:The proportion of medical staff’s hand hygiene compliance and hand hygiene knowledge mastery scores in the observation group were higher than those in the control group(P<0.05);the hand hygiene passing rate in the observation group was higher than that of the control group(P<0.05);the daily amount of hand sanitizer per patient bed and the amount of hand sanitizer used was higher than that of the control group(P<0.05).Conclusion:The PDCA cycle management model for psychiatric medical staff promoted the improvement of hand hygiene compliance and increased their hand hygiene qualifications.It is suitable for further popularization and application in future clinical practice.
文摘It has been proved by the ancient and modemacupuncture practices that needling techniques are ofvital importance in the acupuncture treatment ofdiseases.At present,much has been reported aboutthe researches on acupuncture techniques,which canbe roughly divided into the following three types:reinforcing,reducing,and even methods.Throughlong years of study and clinical practice,Dr.
基金Supported by National Key Research and Development Program of China(Grant No.2018YFE0125600)Zhejiang Provincial Key Research,Develop-ment Program(Grant No.2021C04015)Natural Science Foundation of Zhejiang(Grant No.LZ23E050005).
文摘Teleoperation can assist people to complete various complex tasks in inaccessible or high-risk environments,in which a wearable hand exoskeleton is one of the key devices.Adequate adaptability would be available to enable the master hand exoskeleton to capture the motion of human fingers and reproduce the contact force between the slave hand and its object.This paper presents a novel finger exoskeleton based on the cascading four-link closed-loop kinematic chain.Each finger has an independent closed-loop kinematic chain,and the angle sensors are used to obtain the finger motion including the flexion/extension and the adduction/abduction.The cable tension is changed by the servo motor to transmit the contact force to the fingers in real time.Based on the finger exoskeleton,an adaptive hand exoskeleton is consequently developed.In addition,the hand exoskeleton is tested in a master-slave system.The experiment results show that the adaptive hand exoskeleton can be worn without any mechanical constraints,and the slave hand can follow the motions of each human finger.The accuracy and the real-time capability of the force reproduction are validated.The proposed adaptive hand exoskeleton can be employed as the master hand to remotely control the humanoid five-fingered dexterous slave hand,thus,enabling the teleoperation system to complete complex dexterous manipulation tasks.
基金funded by the National Key Research and Development Program of China(2017YFB1303200)NSFC(81871444,62071241,62075098,and 62001240)+1 种基金Leading-Edge Technology and Basic Research Program of Jiangsu(BK20192004D)Jiangsu Graduate Scientific Research Innovation Programme(KYCX20_1391,KYCX21_1557).
文摘In this article,to reduce the complexity and improve the generalization ability of current gesture recognition systems,we propose a novel SE-CNN attention architecture for sEMG-based hand gesture recognition.The proposed algorithm introduces a temporal squeeze-and-excite block into a simple CNN architecture and then utilizes it to recalibrate the weights of the feature outputs from the convolutional layer.By enhancing important features while suppressing useless ones,the model realizes gesture recognition efficiently.The last procedure of the proposed algorithm is utilizing a simple attention mechanism to enhance the learned representations of sEMG signals to performmulti-channel sEMG-based gesture recognition tasks.To evaluate the effectiveness and accuracy of the proposed algorithm,we conduct experiments involving multi-gesture datasets Ninapro DB4 and Ninapro DB5 for both inter-session validation and subject-wise cross-validation.After a series of comparisons with the previous models,the proposed algorithm effectively increases the robustness with improved gesture recognition performance and generalization ability.
文摘针对采摘机器人自主行走导航过程中,难以准确定位其与果树之间的相对位置,难以准确估计果树树干姿态的问题,提出基于双目eye in hand系统的多角度树干位姿估计方法。利用YOLOv5深度学习方法与半全局块匹配算法识别树干并生成局部点云;利用半径滤波和体素滤波减少树干点云数据;利用闭环式手眼标定方法对双目eye in hand系统进行标定,并对同一树干多角度相机位置的点云数据进行拼接;利用随机抽样一致(RANSAC)算法与无约束最小二乘法估计并优化树干的位置和姿态,获取树干的圆柱体参数。通过对30幅标定板图像进行实验,闭环式手眼标定方法的平均欧式误差为3.7177 mm;采用半径滤波和体素滤波可减少98.470%的点云数据;采用RANSAC算法、圆柱体估计算法拟合树干点云数据,得到圆柱体的半径r=41.2771 mm,R_(MAE)=2.57156 mm,R_(RMSE)=2.98936 mm;无约束最小二乘法优化后r=39.4028 mm,R_(MAE)=1.98955 mm,R_(RMSE)=2.46588 mm。该文通过对双目eye in hand系统进行标定,建立坐标系转换关系,多角度采集环境信息,准确定位机器人与果树之间的相对位置,估计果树树干的姿态。
文摘The first major outbreak of the severely complicated hand,foot and mouth disease(HFMD),primarily caused by enterovirus 71,was reported in Taiwan in 1998.HFMD surveillance is needed to assess the spread of HFMD.The parameters we use in mathematical models are usually classical mathematical parameters,called crisp parameters,which are taken for granted.But any biological or physical phenomenon is best explained by uncertainty.To represent a realistic situation in any mathematical model,fuzzy parameters can be very useful.Many articles have been published on how to control and prevent HFMD from the perspective of public health and statistical modeling.However,few works use fuzzy theory in building models to simulateHFMDdynamics.In this context,we examined anHFMD model with fuzzy parameters.A Non Standard Finite Difference(NSFD)scheme is developed to solve the model.The developed technique retains essential properties such as positivity and dynamic consistency.Numerical simulations are presented to support the analytical results.The convergence and consistency of the proposed method are also discussed.The proposed method converges unconditionally while the many classical methods in the literature do not possess this property.In this regard,our proposed method can be considered as a reliable tool for studying the dynamics of HFMD.
文摘Appearance-based dynamic Hand Gesture Recognition(HGR)remains a prominent area of research in Human-Computer Interaction(HCI).Numerous environmental and computational constraints limit its real-time deployment.In addition,the performance of a model decreases as the subject’s distance from the camera increases.This study proposes a 3D separable Convolutional Neural Network(CNN),considering the model’s computa-tional complexity and recognition accuracy.The 20BN-Jester dataset was used to train the model for six gesture classes.After achieving the best offline recognition accuracy of 94.39%,the model was deployed in real-time while considering the subject’s attention,the instant of performing a gesture,and the subject’s distance from the camera.Despite being discussed in numerous research articles,the distance factor remains unresolved in real-time deployment,which leads to degraded recognition results.In the proposed approach,the distance calculation substantially improves the classification performance by reducing the impact of the subject’s distance from the camera.Additionally,the capability of feature extraction,degree of relevance,and statistical significance of the proposed model against other state-of-the-art models were validated using t-distributed Stochastic Neighbor Embedding(t-SNE),Mathew’s Correlation Coefficient(MCC),and the McNemar test,respectively.We observed that the proposed model exhibits state-of-the-art outcomes and a comparatively high significance level.
文摘The severity of septic arthritis of the hand and the prospects for restoration of joint function are determined by a complex of factors. Among them, the leading role belongs to local changes in tissue structures. This includes the destruction of articular cartilage and bone tissue with the development of osteomyelitis, the involvement of paraarticular soft tissues in the purulent process, and the destruction of the flexor/extensor tendons of the fingers. The currently missing specialized classification of septic arthritis could help in systematizing the diseases, determining treatment tactics, and predicting the results of treatment.The classification of septic arthritis of the hand proposed for discussion is based on the following principle: Joint-Wound-Tendon(Jx Wx Tx);Jx characterizes damage to the osteochondral structures of the joint, Wx is the presence of paraarticular purulent wounds or fistulas, and Tx is destruction of the flexor/extensor tendons of the finger. The classification of the diagnosis makes it possible to assess the nature and severity of damage to the structures of the joint and may also be useful when comparing the results of treatment of septic arthritis of the hand.
基金financed by the Ministry of Education,Science and Technological Development of the Republic of Serbia.
文摘Human hand detection in uncontrolled environments is a challenging visual recognition task due to numerous variations of hand poses and background image clutter.To achieve highly accurate results as well as provide real-time execution,we proposed a deep transfer learning approach over the state-of-the-art deep learning object detector.Our method,denoted as YOLOHANDS,is built on top of the You Only Look Once(YOLO)deep learning architecture,which is modified to adapt to the single class hand detection task.The model transfer is performed by modifying the higher convolutional layers including the last fully connected layer,while initializing lower non-modified layers with the generic pre-trained weights.To address robustness issues,we introduced a comprehensive augmentation procedure over the training image dataset,specifically adapted for the hand detection problem.Experimental evaluation of the proposed method,which is performed on a challenging public dataset,has demonstrated highly accurate results,comparable to the state-of-the-art methods.
基金This research work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(NRF-2022R1A2C1004657).
文摘Hand Gesture Recognition(HGR)is a promising research area with an extensive range of applications,such as surgery,video game techniques,and sign language translation,where sign language is a complicated structured form of hand gestures.The fundamental building blocks of structured expressions in sign language are the arrangement of the fingers,the orientation of the hand,and the hand’s position concerning the body.The importance of HGR has increased due to the increasing number of touchless applications and the rapid growth of the hearing-impaired population.Therefore,real-time HGR is one of the most effective interaction methods between computers and humans.Developing a user-free interface with good recognition performance should be the goal of real-time HGR systems.Nowadays,Convolutional Neural Network(CNN)shows great recognition rates for different image-level classification tasks.It is challenging to train deep CNN networks like VGG-16,VGG-19,Inception-v3,and Efficientnet-B0 from scratch because only some significant labeled image datasets are available for static hand gesture images.However,an efficient and robust hand gesture recognition system of sign language employing finetuned Inception-v3 and Efficientnet-Bo network is proposed to identify hand gestures using a comparative small HGR dataset.Experiments show that Inception-v3 achieved 90%accuracy and 0.93%precision,0.91%recall,and 0.90%f1-score,respectively,while EfficientNet-B0 achieved 99%accuracy and 0.98%,0.97%,0.98%,precision,recall,and f1-score respectively.
