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Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

Computer Modeling in Engineering & Sciences 2024年第8期140卷 1729-1755页

作者： Awais Khan chomyong kim Jung-Yeon kim Ahsan Aziz Yunyoung Nam Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea ICT Convergence Research Center Soonchunhyang UniversityAsan31538Korea Emotional and Intelligent Child Care Convergence Research Center Soonchunhyang UniversityAsan31538Korea

Sleep posture surveillance is crucial for patient comfort,yet current systems face difficulties in providing compre-hensive studies due to the obstruction caused by *** posture assessment remains challenging because of the complex nature of the human body and variations in sleep ***,this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification,thereby enhancing the analysis of body position and *** 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 *** study involves 10 participants under two conditions:with and without ***,the database is normalized into a video *** subsequent step entails training a fine-tuned,pretrained Visual Geometry Group(VGG16)and ResNet50 *** the third phase,the extracted features are utilized for *** 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 ***,the fused vectors are passed to machine learning classifiers for final *** 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 ***,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%.

关键词： Human sleep posture VGG16 deep learning ResNet50 fusion machine learning

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Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

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Computer Modeling in Engineering & Sciences 2024年第6期139卷 3353-3385页

作者： Murugan Palanikumar Nasreen Kausar Dragan Pamucar Seifedine Kadry chomyong kim Yunyoung Nam Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Mathematics Faculty of Arts and ScienceYildiz Technical UniversityEsenlerIstanbul34220Turkey Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Yuan Ze UniversityTaoyuan City320315TaiwanChina Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos1102-2801Lebanon ICT Convergence Research Center Soonchunhyang UniversityAsan31538South Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538South Korea

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 *** is provided of the scoring function,accuracy function and operational laws of the log q-rung *** algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming ***,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 *** 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 *** numbers q are used to express the results of the *** demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

关键词： Vague set aggregating operators euclidean distance hamming distance decision making

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Human Interaction Recognition in Surveillance Videos Using Hybrid Deep Learning and Machine Learning Models

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Computers, Materials & Continua 2024年第10期81卷 773-787页

作者： Vesal Khean chomyong kim Sunjoo Ryu Awais Khan Min Kyung Hong Eun Young kim Joungmin kim Yunyoung Nam Department of ICT Convergence Soonchunhyang UniversityAsan31538Republic of Korea ICT Convergence Research Center Soonchunhyang UniversityAsan31538Republic of Korea Emotional and Intelligent Child Care Convergence Center Soonchunhyang UniversityAsan31538Republic of Korea Department of Occupational Therapy Soonchunhyang UniversityAsan31538Republic of Korea College of Hyangsul Nanum Soonchunhyang UniversityAsan31538Republic of Korea

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 *** 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 *** 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 *** this study,a hybrid Deep Learning(DL)and Machine Learning(ML)model was employed to improve classification accuracy and *** dataset was collected in an indoor environment with four-channel cameras capturing the five types of interactions among 13 *** 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 ***,machine learning models were trained and utilized for interaction classification using six commonly used ML algorithms,including SVM,KNN,RF,DT,NB,and *** results demonstrate a high accuracy of 95.45%in classifying human *** hybrid approach enabled effective learning,resulting in highly accurate performance across different interaction *** work will explore more complex scenarios involving multiple individuals based on the application of this architecture.

关键词： Convolutional neural network deep learning human interaction recognition ResNet skeleton joint key points human pose estimation hybrid deep learning and machine learning

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Feature Fusion Based Deep Transfer Learning Based Human Gait Classification Model

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Intelligent Automation & Soft Computing 2023年第8期37卷 1453-1468页

作者： C.S.S.Anupama Rafina Zakieva Afanasiy Sergin E.Laxmi Lydia Seifedine Kadry chomyong kim Yunyoung Nam Department of Electronics and Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Candidate of Pedagogical Sciences Department of Industrial Electronics and Lighting EngineeringKazan State Power Engineering UniversityKazan420066Russia Department of Theories and Principles of Physical Education and Life Safety North-Eastern Federal University Named After M.K.AmmosovYakutsk677000Russia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

Gait is a biological typical that defines the method by that people *** is the most significant performance which keeps our day-to-day life and physical *** electromyography(sEMG)is a weak bioelectric signal that portrays the functional state between the human muscles and nervous system to any *** classifiers dependent upon sEMG signals are extremely utilized in analysing muscle diseases and as a guide path for recovery *** approaches are established in the works for gait recognition utilizing conventional and deep learning(DL)*** study designs an Enhanced Artificial Algae Algorithm with Hybrid Deep Learning based Human Gait Classification(EAAA-HDLGR)technique on sEMG *** EAAA-HDLGR technique extracts the time domain(TD)and frequency domain(FD)features from the sEMG signals and is *** addition,the EAAA-HDLGR technique exploits the hybrid deep learning(HDL)model for gait *** 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 *** results imply that the EAAA-HDLGR technique accomplishes improved results with the inclusion of EAAA on gait recognition.

关键词： Feature fusion human gait recognition deep learning electromyography signals artificial algae algorithm

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