Human Interaction Recognition(HIR)was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their mov...Human Interaction Recognition(HIR)was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements.HIR requires more sophisticated analysis than Human Action Recognition(HAR)since HAR focuses solely on individual activities like walking or running,while HIR involves the interactions between people.This research aims to develop a robust system for recognizing five common human interactions,such as hugging,kicking,pushing,pointing,and no interaction,from video sequences using multiple cameras.In this study,a hybrid Deep Learning(DL)and Machine Learning(ML)model was employed to improve classification accuracy and generalizability.The dataset was collected in an indoor environment with four-channel cameras capturing the five types of interactions among 13 participants.The data was processed using a DL model with a fine-tuned ResNet(Residual Networks)architecture based on 2D Convolutional Neural Network(CNN)layers for feature extraction.Subsequently,machine learning models were trained and utilized for interaction classification using six commonly used ML algorithms,including SVM,KNN,RF,DT,NB,and XGBoost.The results demonstrate a high accuracy of 95.45%in classifying human interactions.The hybrid approach enabled effective learning,resulting in highly accurate performance across different interaction types.Future work will explore more complex scenarios involving multiple individuals based on the application of this architecture.展开更多
Sleep posture surveillance is crucial for patient comfort,yet current systems face difficulties in providing compre-hensive studies due to the obstruction caused by blankets.Precise posture assessment remains challeng...Sleep posture surveillance is crucial for patient comfort,yet current systems face difficulties in providing compre-hensive studies due to the obstruction caused by blankets.Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns.Consequently,this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification,thereby enhancing the analysis of body position and comfort.This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras,which depict six commonly adopted postures:supine,left log,right log,prone head,prone left,and prone right.The study involves 10 participants under two conditions:with and without blankets.Initially,the database is normalized into a video frame.The subsequent step entails training a fine-tuned,pretrained Visual Geometry Group(VGG16)and ResNet50 model.In the third phase,the extracted features are utilized for classification.The fourth step of the proposed approach employs a serial fusion technique based on the normal distribution to merge the vectors derived from both the RGB and thermal datasets.Finally,the fused vectors are passed to machine learning classifiers for final classification.The dataset,which includes human sleep postures used in this study’s experiments,achieved a 96.7%accuracy rate using the Quadratic Support Vector Machine(QSVM)without the blanket.Moreover,the Linear SVM,when utilized with a blanket,attained an accuracy of 96%.When normal distribution serial fusion was applied to the blanket features,it resulted in a remarkable average accuracy of 99%.展开更多
In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung n...In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.展开更多
Gait is a biological typical that defines the method by that people walk.Walking is the most significant performance which keeps our day-to-day life and physical condition.Surface electromyography(sEMG)is a weak bioel...Gait is a biological typical that defines the method by that people walk.Walking is the most significant performance which keeps our day-to-day life and physical condition.Surface electromyography(sEMG)is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any extent.Gait classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery treatment.Several approaches are established in the works for gait recognition utilizing conventional and deep learning(DL)approaches.This study designs an Enhanced Artificial Algae Algorithm with Hybrid Deep Learning based Human Gait Classification(EAAA-HDLGR)technique on sEMG signals.The EAAA-HDLGR technique extracts the time domain(TD)and frequency domain(FD)features from the sEMG signals and is fused.In addition,the EAAA-HDLGR technique exploits the hybrid deep learning(HDL)model for gait recognition.At last,an EAAA-based hyperparameter optimizer is applied for the HDL model,which is mainly derived from the quasi-oppositional based learning(QOBL)concept,showing the novelty of the work.A brief classifier outcome of the EAAA-HDLGR technique is examined under diverse aspects,and the results indicate improving the EAAA-HDLGR technique.The results imply that the EAAA-HDLGR technique accomplishes improved results with the inclusion of EAAA on gait recognition.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2023-00218176)and the Soonchunhyang University Research Fund.
文摘Human Interaction Recognition(HIR)was one of the challenging issues in computer vision research due to the involvement of multiple individuals and their mutual interactions within video frames generated from their movements.HIR requires more sophisticated analysis than Human Action Recognition(HAR)since HAR focuses solely on individual activities like walking or running,while HIR involves the interactions between people.This research aims to develop a robust system for recognizing five common human interactions,such as hugging,kicking,pushing,pointing,and no interaction,from video sequences using multiple cameras.In this study,a hybrid Deep Learning(DL)and Machine Learning(ML)model was employed to improve classification accuracy and generalizability.The dataset was collected in an indoor environment with four-channel cameras capturing the five types of interactions among 13 participants.The data was processed using a DL model with a fine-tuned ResNet(Residual Networks)architecture based on 2D Convolutional Neural Network(CNN)layers for feature extraction.Subsequently,machine learning models were trained and utilized for interaction classification using six commonly used ML algorithms,including SVM,KNN,RF,DT,NB,and XGBoost.The results demonstrate a high accuracy of 95.45%in classifying human interactions.The hybrid approach enabled effective learning,resulting in highly accurate performance across different interaction types.Future work will explore more complex scenarios involving multiple individuals based on the application of this architecture.
基金supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI)funded by the Ministry of Health&Welfare,Republic of Korea(Grant Number:H12C1831)+2 种基金Korea Institute for Advancement of Technology(KIAT)Grant funded by the Korea Government(MOTIE)(P0012724,HRD Program for Industrial Innovation)the National Research Foundation of Korea(NRF)Grant funded by the Korea Government(MSIT)(No.RS-2023-00218176)the Soonchunhyang University Research Fund.
文摘Sleep posture surveillance is crucial for patient comfort,yet current systems face difficulties in providing compre-hensive studies due to the obstruction caused by blankets.Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns.Consequently,this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification,thereby enhancing the analysis of body position and comfort.This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras,which depict six commonly adopted postures:supine,left log,right log,prone head,prone left,and prone right.The study involves 10 participants under two conditions:with and without blankets.Initially,the database is normalized into a video frame.The subsequent step entails training a fine-tuned,pretrained Visual Geometry Group(VGG16)and ResNet50 model.In the third phase,the extracted features are utilized for classification.The fourth step of the proposed approach employs a serial fusion technique based on the normal distribution to merge the vectors derived from both the RGB and thermal datasets.Finally,the fused vectors are passed to machine learning classifiers for final classification.The dataset,which includes human sleep postures used in this study’s experiments,achieved a 96.7%accuracy rate using the Quadratic Support Vector Machine(QSVM)without the blanket.Moreover,the Linear SVM,when utilized with a blanket,attained an accuracy of 96%.When normal distribution serial fusion was applied to the blanket features,it resulted in a remarkable average accuracy of 99%.
基金supported by the National Research Foundation of Korea(NRF)Grant funded by the Korea government(MSIT)(No.RS-2023-00218176)Korea Institute for Advancement of Technology(KIAT)Grant funded by the Korea government(MOTIE)(P0012724)The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this article.Adescription is provided of the scoring function,accuracy function and operational laws of the log q-rung VS.The algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming distance.Additionally,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify conceptualization.We chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile amnesia.Natural numbers q are used to express the results of the models.To demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.
基金supported by a grant from the Korea Health Technology R&D Project through the KoreaHealth Industry Development Institute (KHIDI)funded by the Ministry of Health&Welfare,Republic of Korea (grant number:HI21C1831)the Soonchunhyang University Research Fund.
文摘Gait is a biological typical that defines the method by that people walk.Walking is the most significant performance which keeps our day-to-day life and physical condition.Surface electromyography(sEMG)is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any extent.Gait classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery treatment.Several approaches are established in the works for gait recognition utilizing conventional and deep learning(DL)approaches.This study designs an Enhanced Artificial Algae Algorithm with Hybrid Deep Learning based Human Gait Classification(EAAA-HDLGR)technique on sEMG signals.The EAAA-HDLGR technique extracts the time domain(TD)and frequency domain(FD)features from the sEMG signals and is fused.In addition,the EAAA-HDLGR technique exploits the hybrid deep learning(HDL)model for gait recognition.At last,an EAAA-based hyperparameter optimizer is applied for the HDL model,which is mainly derived from the quasi-oppositional based learning(QOBL)concept,showing the novelty of the work.A brief classifier outcome of the EAAA-HDLGR technique is examined under diverse aspects,and the results indicate improving the EAAA-HDLGR technique.The results imply that the EAAA-HDLGR technique accomplishes improved results with the inclusion of EAAA on gait recognition.
