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
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
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
The aim of this study is to systematically reveal the differences in the biomechanics of 16 hand regions related to bionic picking of tomatoes.The biomechanical properties(peak loading force,elastic coefficient,maximu...The aim of this study is to systematically reveal the differences in the biomechanics of 16 hand regions related to bionic picking of tomatoes.The biomechanical properties(peak loading force,elastic coefficient,maximum percentage deformation and interaction contact mechanics between human hand and tomato fruit)of each hand region were experimentally measured and covariance analyzed.The results revealed that there were significant variations in the assessed biomechanical properties between the 16 hand regions(p<0.05).The maximum pain force threshold(peak loading force in I2 region)was 5.11 times higher than the minimum pain force threshold(in Th1 region).It was found that each hand region in its normal direction can elastically deform by at least 15.30%.The elastic coefficient of the 16 hand regions ranged from 0.22 to 2.29 N mm−1.The interaction contact force acting on the fruit surface was affected by the selected human factors and fruit features.The obtained covariance models can quantitatively predict all of the above biomechanical properties of 16 hand regions.The findings were closely related to hand grasping performance during tomato picking,such as soft contact,surface interaction,stable and dexterous grasping,provided a foundation for developing a high-performance tomato-picking bionic robotic hand.展开更多
Introduction: Hand hygiene (HH) is an effective way to fight infections in healthcare settings. The general purpose of our study was to explore the knowledge, attitudes and practices of health care providers on HH at ...Introduction: Hand hygiene (HH) is an effective way to fight infections in healthcare settings. The general purpose of our study was to explore the knowledge, attitudes and practices of health care providers on HH at Dapaong regional hospital (DRH). Methodology: This was a prospective, descriptive cross-sectional study conducted from March to June 2022 in the DRH wards. Data were collected using a questionnaire and observation grid. Results: 90 care providers were surveyed. Males and non-physician personnel predominated with 57.8%, and 94.4% respectively. The survey on staff’s knowledge reported: 31.1% of practitioners did not wash their hands on arrival and departure in services. 24% did not know the difference between simple hand washing (SHW) and hygienic hand washing (HHW). 23.3% did not know the type of soap to use for HHW. The caregivers did not know the type of hand washing (HW) required after a septic and non-septic procedure in respectively 41.6%, and 37.8%. They did not know that there are two types of hand antiseptics (45.4%), nor the amount of antiseptic for HW (78.9%). The survey on staff’s attitude regarding HW found that: 70% did not remove all jewels prior HW, and 51.1% did not know that wearing gloves cannot replace the HW. For HW Staff Practice: 62.2% did not wash their hands before treatment. 91.1% did not spread the soap on their hands and forearms after wetting them. 65.55% did not rinse hands from nails to elbows. Conclusion: The HH was poorly known, the attitude of the staff was dangerous in relation to the HH and the practice of HH was very inadequate at the RHC-Dapaong. As a result, there is a need to retrain staff to increase their capacity to prevent care-related infections and enhance patient safety in the hospital.展开更多
As a result of the introduction of new infectious illnesses,key infection prevention measures were implemented.Now,a new coronavirus(SARS-CoV-2)epidemic has expanded swiftly,causing the coronavirus illness 2019(COVID-...As a result of the introduction of new infectious illnesses,key infection prevention measures were implemented.Now,a new coronavirus(SARS-CoV-2)epidemic has expanded swiftly,causing the coronavirus illness 2019(COVID-19).Many microorganisms spread illness via hospital surfaces due to environmental pollution.This virus has been associated to close contact between persons in tight situations such as houses,hospitals,assisted living,and residential institutions.Aside from health care settings,public buildings,faith-based community centers,marketplaces,transportation,and corporate environments are prone to COVID-19 transmission.Physical contact to the sanitizer device may cause for spread Covid virus.That’s why we have pro-posed an automatic fogger mechanism-based hand sanitizer that may able to reduce covid risk.Disinfectant fog will flow when object will pass through the machine.This project will save cost,time and wastage along with Covid spreading risk.This project is about designing a good healthcare system.In recent years,sophisticated automation has influenced the health industry.Health care in poor nations is costly.So,the project is an attempt to tackle this issue.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
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
Tactile and proprioception feedback are essential to myoelectric hand prostheses control for regaining functionality of lost hands of amputees.Current studies focus on tactile feedback,while the lack of appropriate mu...Tactile and proprioception feedback are essential to myoelectric hand prostheses control for regaining functionality of lost hands of amputees.Current studies focus on tactile feedback,while the lack of appropriate multisensory feedback,especially proprioception feedback,limits the grasping quality.Additionally,a typical non-invasive stimulation scheme for sensation feedback uses stimulation on the stationary site of the skin continuously,which can lead to fatigue and adaptation of sensation,further reduces the feedback consistency,and increases the cognitive burden for the subject.Considering the sensitivity and modality matching of sensation,this study presented a multimodal sensations feedback scheme based on hybrid static-dynamic sensation elicited by multisite Transcutaneous Electrical Nerve Stimulation(TENS)to deliver grasping force and joint position feedback.In the proposed scheme,stimulation of single electrode produced only in-loco tactile sensation under the electrode,and the sensation intensity was adjusted according to grasping force;sequential activation of multi-electrodes produced an illusion dynamic sensation of a stimulus moving,and the velocity and direction of movement were adjusted according to finger joint position.Psychometric test results demonstrated the identifiability of stimulus in the proposed scheme.Further,prosthetic hand closed-loop grasping tasks evaluate the effectiveness of the proposed feedback scheme.The results showed that the proposed feedback scheme could substantially improve the grasping accuracy and efficiency.In addition,the study outcomes also demonstrated the benefit of artificial proprioception feedback in grasping rapidity and security.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
Introduction: Congenital talipes equinovarus (CTEV) is the commonest musculoskeletal deformity worldwide. The Ponseti technique is the goal standard for clubfoot treatment. During the correction phase, the provider’s...Introduction: Congenital talipes equinovarus (CTEV) is the commonest musculoskeletal deformity worldwide. The Ponseti technique is the goal standard for clubfoot treatment. During the correction phase, the provider’s right hand manipulates the right foot and the left hand, left foot. Often, one foot is ready for Achilles tenotomy before the other in bilateral clubfoot. Objective: To determine the effect of the provider’s hand dominance would have on bilateral clubfoot treated with the Ponseti technique. Method: This was a prospective cross-sectional study that analyzed idiopathic bilateral clubfoot patients aged 0 - 5 years and treated using the Ponseti technique at FMC Umuahia from October 2019 to September 2020. Informed consent and ethical clearance were obtained. The Pirani scores were obtained and compared at presentation and at each clinic visit. All trained manipulators were right-handed. Two-tailed t-test was used and a p-value less than 0.05 was deemed significant. Results: Forty-seven patients participated in the study with an M:F of 2.6:1 and mean age of 13.79 ± 13.39 months. Thirty-six patients (76.6%) had the same Pirani score on both feet at presentation, while the right and left feet were more severely affected in 8 and 3 cases respectively. The mean number of casts before readiness for tenotomy was 4.95 on the right and 5.28 on the left with p-value of 0.042. Conclusion: Though the right foot had a worse mean Pirani score on presentation, however, it required fewer casts before readiness for tenotomy than the left.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
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.
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.
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supported by a European Marie Curie International Incoming Fellowship(326847 and 912847)a Chinese Universities Scientific Fund(2452018313)an Opening Project of the Key Laboratory of Bionic Engineering(Ministry of Education)of Jilin University(KF20200005).
文摘The aim of this study is to systematically reveal the differences in the biomechanics of 16 hand regions related to bionic picking of tomatoes.The biomechanical properties(peak loading force,elastic coefficient,maximum percentage deformation and interaction contact mechanics between human hand and tomato fruit)of each hand region were experimentally measured and covariance analyzed.The results revealed that there were significant variations in the assessed biomechanical properties between the 16 hand regions(p<0.05).The maximum pain force threshold(peak loading force in I2 region)was 5.11 times higher than the minimum pain force threshold(in Th1 region).It was found that each hand region in its normal direction can elastically deform by at least 15.30%.The elastic coefficient of the 16 hand regions ranged from 0.22 to 2.29 N mm−1.The interaction contact force acting on the fruit surface was affected by the selected human factors and fruit features.The obtained covariance models can quantitatively predict all of the above biomechanical properties of 16 hand regions.The findings were closely related to hand grasping performance during tomato picking,such as soft contact,surface interaction,stable and dexterous grasping,provided a foundation for developing a high-performance tomato-picking bionic robotic hand.
文摘Introduction: Hand hygiene (HH) is an effective way to fight infections in healthcare settings. The general purpose of our study was to explore the knowledge, attitudes and practices of health care providers on HH at Dapaong regional hospital (DRH). Methodology: This was a prospective, descriptive cross-sectional study conducted from March to June 2022 in the DRH wards. Data were collected using a questionnaire and observation grid. Results: 90 care providers were surveyed. Males and non-physician personnel predominated with 57.8%, and 94.4% respectively. The survey on staff’s knowledge reported: 31.1% of practitioners did not wash their hands on arrival and departure in services. 24% did not know the difference between simple hand washing (SHW) and hygienic hand washing (HHW). 23.3% did not know the type of soap to use for HHW. The caregivers did not know the type of hand washing (HW) required after a septic and non-septic procedure in respectively 41.6%, and 37.8%. They did not know that there are two types of hand antiseptics (45.4%), nor the amount of antiseptic for HW (78.9%). The survey on staff’s attitude regarding HW found that: 70% did not remove all jewels prior HW, and 51.1% did not know that wearing gloves cannot replace the HW. For HW Staff Practice: 62.2% did not wash their hands before treatment. 91.1% did not spread the soap on their hands and forearms after wetting them. 65.55% did not rinse hands from nails to elbows. Conclusion: The HH was poorly known, the attitude of the staff was dangerous in relation to the HH and the practice of HH was very inadequate at the RHC-Dapaong. As a result, there is a need to retrain staff to increase their capacity to prevent care-related infections and enhance patient safety in the hospital.
文摘As a result of the introduction of new infectious illnesses,key infection prevention measures were implemented.Now,a new coronavirus(SARS-CoV-2)epidemic has expanded swiftly,causing the coronavirus illness 2019(COVID-19).Many microorganisms spread illness via hospital surfaces due to environmental pollution.This virus has been associated to close contact between persons in tight situations such as houses,hospitals,assisted living,and residential institutions.Aside from health care settings,public buildings,faith-based community centers,marketplaces,transportation,and corporate environments are prone to COVID-19 transmission.Physical contact to the sanitizer device may cause for spread Covid virus.That’s why we have pro-posed an automatic fogger mechanism-based hand sanitizer that may able to reduce covid risk.Disinfectant fog will flow when object will pass through the machine.This project will save cost,time and wastage along with Covid spreading risk.This project is about designing a good healthcare system.In recent years,sophisticated automation has influenced the health industry.Health care in poor nations is costly.So,the project is an attempt to tackle this issue.
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。
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.
基金supported by the National Key Research and Development Program of China(Grant No.2018YFB1307201)the National Natural Science Foundation of China(Grant Nos.51875120,91948302,U1813209).
文摘Tactile and proprioception feedback are essential to myoelectric hand prostheses control for regaining functionality of lost hands of amputees.Current studies focus on tactile feedback,while the lack of appropriate multisensory feedback,especially proprioception feedback,limits the grasping quality.Additionally,a typical non-invasive stimulation scheme for sensation feedback uses stimulation on the stationary site of the skin continuously,which can lead to fatigue and adaptation of sensation,further reduces the feedback consistency,and increases the cognitive burden for the subject.Considering the sensitivity and modality matching of sensation,this study presented a multimodal sensations feedback scheme based on hybrid static-dynamic sensation elicited by multisite Transcutaneous Electrical Nerve Stimulation(TENS)to deliver grasping force and joint position feedback.In the proposed scheme,stimulation of single electrode produced only in-loco tactile sensation under the electrode,and the sensation intensity was adjusted according to grasping force;sequential activation of multi-electrodes produced an illusion dynamic sensation of a stimulus moving,and the velocity and direction of movement were adjusted according to finger joint position.Psychometric test results demonstrated the identifiability of stimulus in the proposed scheme.Further,prosthetic hand closed-loop grasping tasks evaluate the effectiveness of the proposed feedback scheme.The results showed that the proposed feedback scheme could substantially improve the grasping accuracy and efficiency.In addition,the study outcomes also demonstrated the benefit of artificial proprioception feedback in grasping rapidity and security.
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Introduction: Congenital talipes equinovarus (CTEV) is the commonest musculoskeletal deformity worldwide. The Ponseti technique is the goal standard for clubfoot treatment. During the correction phase, the provider’s right hand manipulates the right foot and the left hand, left foot. Often, one foot is ready for Achilles tenotomy before the other in bilateral clubfoot. Objective: To determine the effect of the provider’s hand dominance would have on bilateral clubfoot treated with the Ponseti technique. Method: This was a prospective cross-sectional study that analyzed idiopathic bilateral clubfoot patients aged 0 - 5 years and treated using the Ponseti technique at FMC Umuahia from October 2019 to September 2020. Informed consent and ethical clearance were obtained. The Pirani scores were obtained and compared at presentation and at each clinic visit. All trained manipulators were right-handed. Two-tailed t-test was used and a p-value less than 0.05 was deemed significant. Results: Forty-seven patients participated in the study with an M:F of 2.6:1 and mean age of 13.79 ± 13.39 months. Thirty-six patients (76.6%) had the same Pirani score on both feet at presentation, while the right and left feet were more severely affected in 8 and 3 cases respectively. The mean number of casts before readiness for tenotomy was 4.95 on the right and 5.28 on the left with p-value of 0.042. Conclusion: Though the right foot had a worse mean Pirani score on presentation, however, it required fewer casts before readiness for tenotomy than the left.
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.
