The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable pr...The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.展开更多
Most fish exhibit remarkable morphological diversity,which is often influenced by genetic variation and ecological pressures.Consequently,these are the outcomes of organisms’responses to their environment.Meanwhile,m...Most fish exhibit remarkable morphological diversity,which is often influenced by genetic variation and ecological pressures.Consequently,these are the outcomes of organisms’responses to their environment.Meanwhile,modern morphometrics can quantify shape variation within species of the same group.This study aims to determine the body shape variation of Glossogobius giuris from Lake Mainit,Agusan Del Norte,Philippines.60 adult,uniform-sized fish samples were collected and subjected to standardized laboratory procedures.Further,the samples were digitized for 16 homologous landmark points and loaded into Symmetry Asymmetry Geometric Data(SAGE)Software.Across the tested factors—individuals,sides,and individual x sides—result shows that shape variations among individuals were highly significant(F=2.1045,p<0.0001),along with among males(F=3.2711,p<0.0001).Females exhibited higher Fluctuating Asymmetry(FA)(F=18.99,p<0.0001)compared to males(F=7.0964,p<0.0001).It suggests morphological shape differences across the sexes,and the shape variation observed could be a response to environmental perturbations.Shape variations were associated with swimming,food hunting,and predator defense.Moreover,Principal Component Analysis(PCA)demonstrates higher scores of FA in females(81.96%)than in males(74.76%).It was noticed that females had a high fluctuating asymmetry.It might be due to various physiological and ecological pressures compared to males.The observed levels of directional and fluctuating asymmetry in males and females,respectively,may indicate sex-linked morphological and developmental processes,which are important to consider in ecological or evolutionary contexts.Thus,utilizing geometric morphometrics can depict subtle differences across the same populations.展开更多
Senvagė Park,located in the old town of Panevėžys,Lithuania,underwent a major renovation(翻新)in 2022,transforming it into one of the citys key landmarks.The red brick loop(环形)path around the pond has become the mos...Senvagė Park,located in the old town of Panevėžys,Lithuania,underwent a major renovation(翻新)in 2022,transforming it into one of the citys key landmarks.The red brick loop(环形)path around the pond has become the most popular spot for recreational walks,while new piers,bridges and wooden steps allow visitors to get closer to the water.While Senvagė Park was always a scenic spot,decades of neglect had left it with deteriorating infrastructure(基础设施)and limited appeal.Accessibility was also a challenge due to height differences and numerous stairs.展开更多
As the Mars probe,which has limited on-board ability in computation is unable to carry out the large-scale landmark solution,it is necessary to achieve optimal selection of landmarks while ensuring autonomous navigati...As the Mars probe,which has limited on-board ability in computation is unable to carry out the large-scale landmark solution,it is necessary to achieve optimal selection of landmarks while ensuring autonomous navigation accuracy during landing phase.This paper proposes an optimal landmark selection method based on the observability matrix for the Mars probe.Firstly,an observability matrix for navigation system is constructed with Fisher information quantity.Secondly,the optimal configuration of the landmark distribution is given by maximizing the scalar function of the observability matrix.Based on the optimal configuration,the greedy algorithm is used to determine the number of the landmarks at each moment adaptively.In addition,considering the fact that the number of the observable landmarks gradually decreases during the landing process,the convergence threshold of the greedy algorithm is set to a dynamic value regarding landing time.Finally,mathematical simulation verification is conducted,and the results show that the proposed optimal landmark selection method has higher navigation accuracy compared with the random landmark selection method.It can effectively suppress the influence of the measurement model errors and achieve a higher landing accuracy.展开更多
In order to address the challenges encountered in visual navigation for asteroid landing using traditional point features,such as significant recognition and extraction errors,low computational efficiency,and limited ...In order to address the challenges encountered in visual navigation for asteroid landing using traditional point features,such as significant recognition and extraction errors,low computational efficiency,and limited navigation accuracy,a novel approach for multi-type fusion visual navigation is proposed.This method aims to overcome the limitations of single-type features and enhance navigation accuracy.Analytical criteria for selecting multi-type features are introduced,which simultaneously improve computational efficiency and system navigation accuracy.Concerning pose estimation,both absolute and relative pose estimation methods based on multi-type feature fusion are proposed,and multi-type feature normalization is established,which significantly improves system navigation accuracy and lays the groundwork for flexible application of joint absolute-relative estimation.The feasibility and effectiveness of the proposed method are validated through simulation experiments through 4769 Castalia.展开更多
Driver distraction is a leading cause of traffic accidents,with fatigue being a significant contributor.This paper introduces a novel method for detecting driver distraction by analyzing facial features using machine ...Driver distraction is a leading cause of traffic accidents,with fatigue being a significant contributor.This paper introduces a novel method for detecting driver distraction by analyzing facial features using machine deep learning and 68 face model.The proposed system assesses driver tiredness by measuring the distance between key facial landmarks,such as the distance between the eyes and the angle of the mouth,to evaluate signs of drowsiness or disengagement.Real-time video feed analysis allows for continuous monitoring of the driver’s face,enabling the system to detect behavioral cues associated with distraction,such as eye closures or changes in facial expressions.The effectiveness of this method is demonstrated through a series of experiments on a dataset of driver videos,which proves that the approach can accurately assess tiredness and distraction levels under various driving conditions.By focusing on facial landmarks,the system is computationally efficient and capable of operating in real-time,making it a practical solution for in-vehicle safety systems.This paper discusses the system’s performance,limitations,and potential for future enhancements,including integration with other in-vehicle technologies to provide comprehensive driver monitoring.展开更多
Objective To determine the correlation between traditional Chinese medicine(TCM)inspec-tion of spirit classification and the severity grade of depression based on facial features,offer-ing insights for intelligent int...Objective To determine the correlation between traditional Chinese medicine(TCM)inspec-tion of spirit classification and the severity grade of depression based on facial features,offer-ing insights for intelligent intergrated TCM and western medicine diagnosis of depression.Methods Using the Audio-Visual Emotion Challenge and Workshop(AVEC 2014)public dataset on depression,which conclude 150 interview videos,the samples were classified ac-cording to the TCM inspection of spirit classification:Deshen(得神,presence of spirit),Shaoshen(少神,insufficiency of spirit),and Shenluan(神乱,confusion of spirit).Meanwhile,based on Beck Depression Inventory-II(BDI-II)score for the severity grade of depression,the samples were divided into minimal(0-13,Q1),mild(14-19,Q2),moderate(20-28,Q3),and severe(29-63,Q4).Sixty-eight landmarks were extracted with a ResNet-50 network,and the feature extracion mode was stadardized.Random forest and support vectior machine(SVM)classifiers were used to predict TCM inspection of spirit classification and the severity grade of depression,respectively.A Chi-square test and Apriori association rule mining were then applied to quantify and explore the relationships.Results The analysis revealed a statistically significant and moderately strong association be-tween TCM spirit classification and the severity grade of depression,as confirmed by a Chi-square test(χ^(2)=14.04,P=0.029)with a Cramer’s V effect size of 0.243.Further exploration us-ing association rule mining identified the most compelling rule:“moderate depression(Q3)→Shenluan”.This rule demonstrated a support level of 5%,indicating this specific co-occur-rence was present in 5%of the cohort.Crucially,it achieved a high Confidence of 86%,mean-ing that among patients diagnosed with Q3,86%exhibited the Shenluan pattern according to TCM assessment.The substantial Lift of 2.37 signifies that the observed likelihood of Shenlu-an manifesting in Q3 patients is 2.37 times higher than would be expected by chance if these states were independent-compelling evidence of a highly non-random association.Conse-quently,Shenluan emerges as a distinct and core TCM diagnostic manifestation strongly linked to Q3,forming a clinically significant phenotype within this patient subgroup.展开更多
Depression is a mental psychological disorder that may cause a physical disorder or lead to death.It is highly impactful on the socialeconomical life of a person;therefore,its effective and timely detection is needful...Depression is a mental psychological disorder that may cause a physical disorder or lead to death.It is highly impactful on the socialeconomical life of a person;therefore,its effective and timely detection is needful.Despite speech and gait,facial expressions have valuable clues to depression.This study proposes a depression detection system based on facial expression analysis.Facial features have been used for depression detection using Support Vector Machine(SVM)and Convolutional Neural Network(CNN).We extracted micro-expressions using Facial Action Coding System(FACS)as Action Units(AUs)correlated with the sad,disgust,and contempt features for depression detection.A CNN-based model is also proposed in this study to auto classify depressed subjects from images or videos in real-time.Experiments have been performed on the dataset obtained from Bahawal Victoria Hospital,Bahawalpur,Pakistan,as per the patient health questionnaire depression scale(PHQ-8);for inferring the mental condition of a patient.The experiments revealed 99.9%validation accuracy on the proposed CNN model,while extracted features obtained 100%accuracy on SVM.Moreover,the results proved the superiority of the reported approach over state-of-the-art methods.展开更多
In order to study the relationship between landmarks and spatial memory in short-nosed fruit bat, Cynopterus sphinx （Megachiroptera, Pteropodidae）, we simulated a foraging environment in the laboratory. Different la...In order to study the relationship between landmarks and spatial memory in short-nosed fruit bat, Cynopterus sphinx （Megachiroptera, Pteropodidae）, we simulated a foraging environment in the laboratory. Different landmarks were placed to gauge the spatial memory of C. sphinx. We changed the number of landmarks every day with 0 landmarks again on the fifth day （from 0, 2, 4, 8 to 0）. Individuals from the control group were exposed to the identical artificial foraging environment, but without landmarks. The results indicated that there was significant correlation between the time of the first foraging and the experimental days in both groups （Pearson Correlation： experimental group： r=-0.593, P〈0.01; control group： r=-0.581, P〈0.01）. There was no significant correlation between the success rates of foraging and the experimental days in experimental groups （Pearson Correlation： r=0.177, P〉0.05）, but there was significant correlation between the success rates of foraging and the experimental days in the control groups （Pearson Correlation： r=0.445, P〈0.05）. There was no significant difference for the first foraging time between experimental and control groups （GLM： F0.05,1=4.703, P〉0.05）; also, there was no significant difference in success rates of foraging between these two groups （GLM： F0.05,1=0.849,P〉0.05）. The results of our experiment suggest that spatial memory in C. sphinx was formed gradually and that the placed landmarks appeared to have no discernable effects on the memory of the foraging space.展开更多
文摘The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.
文摘Most fish exhibit remarkable morphological diversity,which is often influenced by genetic variation and ecological pressures.Consequently,these are the outcomes of organisms’responses to their environment.Meanwhile,modern morphometrics can quantify shape variation within species of the same group.This study aims to determine the body shape variation of Glossogobius giuris from Lake Mainit,Agusan Del Norte,Philippines.60 adult,uniform-sized fish samples were collected and subjected to standardized laboratory procedures.Further,the samples were digitized for 16 homologous landmark points and loaded into Symmetry Asymmetry Geometric Data(SAGE)Software.Across the tested factors—individuals,sides,and individual x sides—result shows that shape variations among individuals were highly significant(F=2.1045,p<0.0001),along with among males(F=3.2711,p<0.0001).Females exhibited higher Fluctuating Asymmetry(FA)(F=18.99,p<0.0001)compared to males(F=7.0964,p<0.0001).It suggests morphological shape differences across the sexes,and the shape variation observed could be a response to environmental perturbations.Shape variations were associated with swimming,food hunting,and predator defense.Moreover,Principal Component Analysis(PCA)demonstrates higher scores of FA in females(81.96%)than in males(74.76%).It was noticed that females had a high fluctuating asymmetry.It might be due to various physiological and ecological pressures compared to males.The observed levels of directional and fluctuating asymmetry in males and females,respectively,may indicate sex-linked morphological and developmental processes,which are important to consider in ecological or evolutionary contexts.Thus,utilizing geometric morphometrics can depict subtle differences across the same populations.
文摘Senvagė Park,located in the old town of Panevėžys,Lithuania,underwent a major renovation(翻新)in 2022,transforming it into one of the citys key landmarks.The red brick loop(环形)path around the pond has become the most popular spot for recreational walks,while new piers,bridges and wooden steps allow visitors to get closer to the water.While Senvagė Park was always a scenic spot,decades of neglect had left it with deteriorating infrastructure(基础设施)and limited appeal.Accessibility was also a challenge due to height differences and numerous stairs.
基金supported by the National Natural Science Foundation of China(62203458)the Stabilisation Support Project of the Bureau of Science and Industry(HTKJ2023KL502012)the Youth Autonomous Innovation Science Fund(ZK23-01).
文摘As the Mars probe,which has limited on-board ability in computation is unable to carry out the large-scale landmark solution,it is necessary to achieve optimal selection of landmarks while ensuring autonomous navigation accuracy during landing phase.This paper proposes an optimal landmark selection method based on the observability matrix for the Mars probe.Firstly,an observability matrix for navigation system is constructed with Fisher information quantity.Secondly,the optimal configuration of the landmark distribution is given by maximizing the scalar function of the observability matrix.Based on the optimal configuration,the greedy algorithm is used to determine the number of the landmarks at each moment adaptively.In addition,considering the fact that the number of the observable landmarks gradually decreases during the landing process,the convergence threshold of the greedy algorithm is set to a dynamic value regarding landing time.Finally,mathematical simulation verification is conducted,and the results show that the proposed optimal landmark selection method has higher navigation accuracy compared with the random landmark selection method.It can effectively suppress the influence of the measurement model errors and achieve a higher landing accuracy.
基金supported by the National Natural Science Foundation of China(No.U2037602)。
文摘In order to address the challenges encountered in visual navigation for asteroid landing using traditional point features,such as significant recognition and extraction errors,low computational efficiency,and limited navigation accuracy,a novel approach for multi-type fusion visual navigation is proposed.This method aims to overcome the limitations of single-type features and enhance navigation accuracy.Analytical criteria for selecting multi-type features are introduced,which simultaneously improve computational efficiency and system navigation accuracy.Concerning pose estimation,both absolute and relative pose estimation methods based on multi-type feature fusion are proposed,and multi-type feature normalization is established,which significantly improves system navigation accuracy and lays the groundwork for flexible application of joint absolute-relative estimation.The feasibility and effectiveness of the proposed method are validated through simulation experiments through 4769 Castalia.
文摘Driver distraction is a leading cause of traffic accidents,with fatigue being a significant contributor.This paper introduces a novel method for detecting driver distraction by analyzing facial features using machine deep learning and 68 face model.The proposed system assesses driver tiredness by measuring the distance between key facial landmarks,such as the distance between the eyes and the angle of the mouth,to evaluate signs of drowsiness or disengagement.Real-time video feed analysis allows for continuous monitoring of the driver’s face,enabling the system to detect behavioral cues associated with distraction,such as eye closures or changes in facial expressions.The effectiveness of this method is demonstrated through a series of experiments on a dataset of driver videos,which proves that the approach can accurately assess tiredness and distraction levels under various driving conditions.By focusing on facial landmarks,the system is computationally efficient and capable of operating in real-time,making it a practical solution for in-vehicle safety systems.This paper discusses the system’s performance,limitations,and potential for future enhancements,including integration with other in-vehicle technologies to provide comprehensive driver monitoring.
基金Research and Development Plan of Key Areas of Hunan Science and Technology Department (2022SK2044)Clinical Research Center for Depressive Disorder in Hunan Province (2021SK4022)。
文摘Objective To determine the correlation between traditional Chinese medicine(TCM)inspec-tion of spirit classification and the severity grade of depression based on facial features,offer-ing insights for intelligent intergrated TCM and western medicine diagnosis of depression.Methods Using the Audio-Visual Emotion Challenge and Workshop(AVEC 2014)public dataset on depression,which conclude 150 interview videos,the samples were classified ac-cording to the TCM inspection of spirit classification:Deshen(得神,presence of spirit),Shaoshen(少神,insufficiency of spirit),and Shenluan(神乱,confusion of spirit).Meanwhile,based on Beck Depression Inventory-II(BDI-II)score for the severity grade of depression,the samples were divided into minimal(0-13,Q1),mild(14-19,Q2),moderate(20-28,Q3),and severe(29-63,Q4).Sixty-eight landmarks were extracted with a ResNet-50 network,and the feature extracion mode was stadardized.Random forest and support vectior machine(SVM)classifiers were used to predict TCM inspection of spirit classification and the severity grade of depression,respectively.A Chi-square test and Apriori association rule mining were then applied to quantify and explore the relationships.Results The analysis revealed a statistically significant and moderately strong association be-tween TCM spirit classification and the severity grade of depression,as confirmed by a Chi-square test(χ^(2)=14.04,P=0.029)with a Cramer’s V effect size of 0.243.Further exploration us-ing association rule mining identified the most compelling rule:“moderate depression(Q3)→Shenluan”.This rule demonstrated a support level of 5%,indicating this specific co-occur-rence was present in 5%of the cohort.Crucially,it achieved a high Confidence of 86%,mean-ing that among patients diagnosed with Q3,86%exhibited the Shenluan pattern according to TCM assessment.The substantial Lift of 2.37 signifies that the observed likelihood of Shenlu-an manifesting in Q3 patients is 2.37 times higher than would be expected by chance if these states were independent-compelling evidence of a highly non-random association.Conse-quently,Shenluan emerges as a distinct and core TCM diagnostic manifestation strongly linked to Q3,forming a clinically significant phenotype within this patient subgroup.
文摘Depression is a mental psychological disorder that may cause a physical disorder or lead to death.It is highly impactful on the socialeconomical life of a person;therefore,its effective and timely detection is needful.Despite speech and gait,facial expressions have valuable clues to depression.This study proposes a depression detection system based on facial expression analysis.Facial features have been used for depression detection using Support Vector Machine(SVM)and Convolutional Neural Network(CNN).We extracted micro-expressions using Facial Action Coding System(FACS)as Action Units(AUs)correlated with the sad,disgust,and contempt features for depression detection.A CNN-based model is also proposed in this study to auto classify depressed subjects from images or videos in real-time.Experiments have been performed on the dataset obtained from Bahawal Victoria Hospital,Bahawalpur,Pakistan,as per the patient health questionnaire depression scale(PHQ-8);for inferring the mental condition of a patient.The experiments revealed 99.9%validation accuracy on the proposed CNN model,while extracted features obtained 100%accuracy on SVM.Moreover,the results proved the superiority of the reported approach over state-of-the-art methods.
基金supported by the National Natural Science Foundation of China(NSFC,No30800102)Natural Science Foundation of Hainan Province(309026)
文摘In order to study the relationship between landmarks and spatial memory in short-nosed fruit bat, Cynopterus sphinx （Megachiroptera, Pteropodidae）, we simulated a foraging environment in the laboratory. Different landmarks were placed to gauge the spatial memory of C. sphinx. We changed the number of landmarks every day with 0 landmarks again on the fifth day （from 0, 2, 4, 8 to 0）. Individuals from the control group were exposed to the identical artificial foraging environment, but without landmarks. The results indicated that there was significant correlation between the time of the first foraging and the experimental days in both groups （Pearson Correlation： experimental group： r=-0.593, P〈0.01; control group： r=-0.581, P〈0.01）. There was no significant correlation between the success rates of foraging and the experimental days in experimental groups （Pearson Correlation： r=0.177, P〉0.05）, but there was significant correlation between the success rates of foraging and the experimental days in the control groups （Pearson Correlation： r=0.445, P〈0.05）. There was no significant difference for the first foraging time between experimental and control groups （GLM： F0.05,1=4.703, P〉0.05）; also, there was no significant difference in success rates of foraging between these two groups （GLM： F0.05,1=0.849,P〉0.05）. The results of our experiment suggest that spatial memory in C. sphinx was formed gradually and that the placed landmarks appeared to have no discernable effects on the memory of the foraging space.
