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The recent growth of the elderly population has led to the requirement for constant home monitoring as solitary living becomes popular. This protects older people who live alone from unwanted instances such as falling or deterioration caused by some diseases. However, although wearable devices and camera-based systems can provide relatively precise information about human motion, they invade the privacy of the elderly. One way to detect the abnormal behavior of elderly residents under the condition of maintaining privacy is to equip the resident’s house with an Internet of Things system based on a non-invasive binary motion sensor array. We propose to concatenate external features (previous activity and begin time-stamp) along with extracted features with a bi-directional long short-term memory (Bi-LSTM) neural network to recognize the activities of daily living with a higher accuracy. The concatenated features are classified by a fully connected neural network (FCNN). The proposed model was evaluated on open dataset from the Center for Advanced Studies in Adaptive Systems (CASAS) at Washington State University. The experimental results show that the proposed method outperformed state-of-the-art models with a margin of more than 6.25% of the F1 score on the same dataset.
Tan-Hsu Tan; Luubaatar Badarch; Wei-Xiang Zeng; Munkhjargal Gochoo; Fady Alnajjar; Jun-Wei Hsieh. Binary Sensors-Based Privacy-Preserved Activity Recognition of Elderly Living Alone Using an RNN. Sensors 2021, 21, 5371 .
AMA StyleTan-Hsu Tan, Luubaatar Badarch, Wei-Xiang Zeng, Munkhjargal Gochoo, Fady Alnajjar, Jun-Wei Hsieh. Binary Sensors-Based Privacy-Preserved Activity Recognition of Elderly Living Alone Using an RNN. Sensors. 2021; 21 (16):5371.
Chicago/Turabian StyleTan-Hsu Tan; Luubaatar Badarch; Wei-Xiang Zeng; Munkhjargal Gochoo; Fady Alnajjar; Jun-Wei Hsieh. 2021. "Binary Sensors-Based Privacy-Preserved Activity Recognition of Elderly Living Alone Using an RNN." Sensors 21, no. 16: 5371.
This work presents the grouping of dependent tasks into a cluster using the Bayesian analysis model to solve the affinity scheduling problem in heterogeneous multicore systems. The non-affinity scheduling of tasks has a negative impact as the overall execution time for the tasks increases. Furthermore, non-affinity-based scheduling also limits the potential for data reuse in the caches so it becomes necessary to bring the same data into the caches multiple times. In heterogeneous multicore systems, it is essential to address the load balancing problem as all cores are operating at varying frequencies. We propose two techniques to solve the load balancing issue, one being designated “chunk-based scheduler” (CBS) which is applied to the heterogeneous systems while the other system is “quantum-based intra-core task migration” (QBICTM) where each task is given a fair and equal chance to run on the fastest core. Results show 30–55% improvement in the average execution time of the tasks by applying our CBS or QBICTM scheduler compare to other traditional schedulers when compared using the same operating system.
Sohaib Abbasi; Shaharyar Kamal; Munkhjargal Gochoo; Ahmad Jalal; KiBum Kim. Affinity-Based Task Scheduling on Heterogeneous Multicore Systems Using CBS and QBICTM. Applied Sciences 2021, 11, 5740 .
AMA StyleSohaib Abbasi, Shaharyar Kamal, Munkhjargal Gochoo, Ahmad Jalal, KiBum Kim. Affinity-Based Task Scheduling on Heterogeneous Multicore Systems Using CBS and QBICTM. Applied Sciences. 2021; 11 (12):5740.
Chicago/Turabian StyleSohaib Abbasi; Shaharyar Kamal; Munkhjargal Gochoo; Ahmad Jalal; KiBum Kim. 2021. "Affinity-Based Task Scheduling on Heterogeneous Multicore Systems Using CBS and QBICTM." Applied Sciences 11, no. 12: 5740.
Automatic head tracking and counting using depth imagery has various practical applications in security, logistics, queue management, space utilization and visitor counting. However, no currently available system can clearly distinguish between a human head and other objects in order to track and count people accurately. For this reason, we propose a novel system that can track people by monitoring their heads and shoulders in complex environments and also count the number of people entering and exiting the scene. Our system is split into six phases; at first, preprocessing is done by converting videos of a scene into frames and removing the background from the video frames. Second, heads are detected using Hough Circular Gradient Transform, and shoulders are detected by HOG based symmetry methods. Third, three robust features, namely, fused joint HOG-LBP, Energy based Point clouds and Fused intra-inter trajectories are extracted. Fourth, the Apriori-Association is implemented to select the best features. Fifth, deep learning is used for accurate people tracking. Finally, heads are counted using Cross-line judgment. The system was tested on three benchmark datasets: the PCDS dataset, the MICC people counting dataset and the GOTPD dataset and counting accuracy of 98.40%, 98%, and 99% respectively was achieved. Our system obtained remarkable results.
Munkhjargal Gochoo; Syeda Rizwan; Yazeed Ghadi; Ahmad Jalal; KiBum Kim. A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras. Applied Sciences 2021, 11, 5503 .
AMA StyleMunkhjargal Gochoo, Syeda Rizwan, Yazeed Ghadi, Ahmad Jalal, KiBum Kim. A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras. Applied Sciences. 2021; 11 (12):5503.
Chicago/Turabian StyleMunkhjargal Gochoo; Syeda Rizwan; Yazeed Ghadi; Ahmad Jalal; KiBum Kim. 2021. "A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras." Applied Sciences 11, no. 12: 5503.
To prevent disasters and to control and supervise crowds, automated video surveillance has become indispensable. In today’s complex and crowded environments, manual surveillance and monitoring systems are inefficient, labor intensive, and unwieldy. Automated video surveillance systems offer promising solutions, but challenges remain. One of the major challenges is the extraction of true foregrounds of pixels representing humans only. Furthermore, to accurately understand and interpret crowd behavior, human crowd behavior (HCB) systems require robust feature extraction methods, along with powerful and reliable decision-making classifiers. In this paper, we describe our approach to these issues by presenting a novel Particles Force Model for multi-person tracking, a vigorous fusion of global and local descriptors, along with a robust improved entropy classifier for detecting and interpreting crowd behavior. In the proposed model, necessary preprocessing steps are followed by the application of a first distance algorithm for the removal of background clutter; true-foreground elements are then extracted via a Particles Force Model. The detected human forms are then counted by labeling and performing cluster estimation, using a K-nearest neighbors search algorithm. After that, the location of all the human silhouettes is fixed and, using the Jaccard similarity index and normalized cross-correlation as a cost function, multi-person tracking is performed. For HCB detection, we introduced human crowd contour extraction as a global feature and a particles gradient motion (PGD) descriptor, along with geometrical and speeded up robust features (SURF) for local features. After features were extracted, we applied bat optimization for optimal features, which also works as a pre-classifier. Finally, we introduced a robust improved entropy classifier for decision making and automated crowd behavior detection in smart surveillance systems. We evaluated the performance of our proposed system on a publicly available benchmark PETS2009 and UMN dataset. Experimental results show that our system performed better compared to existing well-known state-of-the-art methods by achieving higher accuracy rates. The proposed system can be deployed to great benefit in numerous public places, such as airports, shopping malls, city centers, and train stations to control, supervise, and protect crowds.
Faisal Abdullah; Yazeed Ghadi; Munkhjargal Gochoo; Ahmad Jalal; KiBum Kim. Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier. Entropy 2021, 23, 628 .
AMA StyleFaisal Abdullah, Yazeed Ghadi, Munkhjargal Gochoo, Ahmad Jalal, KiBum Kim. Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier. Entropy. 2021; 23 (5):628.
Chicago/Turabian StyleFaisal Abdullah; Yazeed Ghadi; Munkhjargal Gochoo; Ahmad Jalal; KiBum Kim. 2021. "Multi-Person Tracking and Crowd Behavior Detection via Particles Gradient Motion Descriptor and Improved Entropy Classifier." Entropy 23, no. 5: 628.
Based on the rapid increase in the demand for people counting and tracking systems for surveillance applications, there is a critical need for more accurate, efficient, and reliable systems. The main goal of this study was to develop an accurate, sustainable, and efficient system that is capable of error-free counting and tracking in public places. The major objective of this research is to develop a system that can perform well in different orientations, different densities, and different backgrounds. We propose an accurate and novel approach consisting of preprocessing, object detection, people verification, particle flow, feature extraction, self-organizing map (SOM) based clustering, people counting, and people tracking. Initially, filters are applied to preprocess images and detect objects. Next, random particles are distributed, and features are extracted. Subsequently, particle flows are clustered using a self-organizing map, and people counting and tracking are performed based on motion trajectories. Experimental results on the PETS-2009 dataset reveal an accuracy of 86.9% for people counting and 87.5% for people tracking, while experimental results on the TUD-Pedestrian dataset yield 94.2% accuracy for people counting and 94.5% for people tracking. The proposed system is a useful tool for medium-density crowds and can play a vital role in people counting and tracking applications.
Mahwish Pervaiz; Yazeed Ghadi; Munkhjargal Gochoo; Ahmad Jalal; Shaharyar Kamal; Dong-Seong Kim. A Smart Surveillance System for People Counting and Tracking Using Particle Flow and Modified SOM. Sustainability 2021, 13, 5367 .
AMA StyleMahwish Pervaiz, Yazeed Ghadi, Munkhjargal Gochoo, Ahmad Jalal, Shaharyar Kamal, Dong-Seong Kim. A Smart Surveillance System for People Counting and Tracking Using Particle Flow and Modified SOM. Sustainability. 2021; 13 (10):5367.
Chicago/Turabian StyleMahwish Pervaiz; Yazeed Ghadi; Munkhjargal Gochoo; Ahmad Jalal; Shaharyar Kamal; Dong-Seong Kim. 2021. "A Smart Surveillance System for People Counting and Tracking Using Particle Flow and Modified SOM." Sustainability 13, no. 10: 5367.
The monitoring of human physical activities using wearable sensors, such as inertial-based sensors, plays a significant role in various current and potential applications. These applications include physical health tracking, surveillance systems, and robotic assistive technologies. Despite the wide range of applications, classification and recognition of human activities remains imprecise and this may contribute to unfavorable reactions and responses. To improve the recognition of human activities, we designed a dataset in which ten participants (five male and five female) performed 11 different activities wearing three body-worn inertial sensors in different locations on the body. Our model extracts data via a hierarchical feature-based technique. These features include time, wavelet, and time-frequency domains, respectively. Stochastic gradient descent (SGD) is then introduced to optimize selective features. The selected features with optimized patterns are further processed by multi-layered kernel sliding perceptron to develop adaptive learning for the classification of physical human activities. Our proposed model was experimentally evaluated and applied on three benchmark datasets: IM-WSHA, a self-annotated dataset, PAMAP2 dataset which is comprised of daily living activities, and an HuGaDB, a dataset which contains physical activities for aging people. The experimental results show that the proposed method achieves better results and outperforms others in terms of recognition accuracy, achieving an accuracy rate of 83.18%, 94.16%, and 92.50% respectively, when IM-WSHA, PAMAP2, and HuGaDB datasets are applied.
Munkhjargal Gochoo; Sheikh Badar Ud Din Tahir; Ahmad Jalal; KiBum Kim. Monitoring Real-Time Personal Locomotion Behaviors Over Smart Indoor-Outdoor Environments Via Body-Worn Sensors. IEEE Access 2021, 9, 70556 -70570.
AMA StyleMunkhjargal Gochoo, Sheikh Badar Ud Din Tahir, Ahmad Jalal, KiBum Kim. Monitoring Real-Time Personal Locomotion Behaviors Over Smart Indoor-Outdoor Environments Via Body-Worn Sensors. IEEE Access. 2021; 9 ():70556-70570.
Chicago/Turabian StyleMunkhjargal Gochoo; Sheikh Badar Ud Din Tahir; Ahmad Jalal; KiBum Kim. 2021. "Monitoring Real-Time Personal Locomotion Behaviors Over Smart Indoor-Outdoor Environments Via Body-Worn Sensors." IEEE Access 9, no. : 70556-70570.
The purpose of this study was to examine the current state of integrated science, technology, engineering, arts, and mathematics (STEAM) education. We conducted an extensive review of the literature, followed by document analysis to construct concepts and themes associated with prospects, priorities, processes, and problems of STEAM education. The analysis of STEAM learning concepts from the literature provided three sub-constructs under the prospects – STEAM movement, the purpose of STEAM education, and benefits of STEAM education. The sub-constructs under priorities of STEAM education were – curriculum integration in STEAM and STEAM education as a curriculum reform. The sub-constructs of STEAM education as a process were – the pedagogical process and assessment in STEAM education. The sub-constructs of the problems in STEAM education were – critiques against STEAM education and the challenges of STEAM education. Finally, we discussed findings and presented some implications of STEAM education.
Shashidhar Belbase; Bhesh Raj Mainali; Wandee Kasemsukpipat; Hassan Tairab; Munkhjargal Gochoo; Adeeb Jarrah. At the dawn of science, technology, engineering, arts, and mathematics (STEAM) education: prospects, priorities, processes, and problems. International Journal of Mathematical Education in Science and Technology 2021, 1 -37.
AMA StyleShashidhar Belbase, Bhesh Raj Mainali, Wandee Kasemsukpipat, Hassan Tairab, Munkhjargal Gochoo, Adeeb Jarrah. At the dawn of science, technology, engineering, arts, and mathematics (STEAM) education: prospects, priorities, processes, and problems. International Journal of Mathematical Education in Science and Technology. 2021; ():1-37.
Chicago/Turabian StyleShashidhar Belbase; Bhesh Raj Mainali; Wandee Kasemsukpipat; Hassan Tairab; Munkhjargal Gochoo; Adeeb Jarrah. 2021. "At the dawn of science, technology, engineering, arts, and mathematics (STEAM) education: prospects, priorities, processes, and problems." International Journal of Mathematical Education in Science and Technology , no. : 1-37.
Research on the human activity recognition could be utilized for the monitoring of elderly people living alone to reduce the cost of home care. Video sensors can be easily deployed in the different zones of houses to achieve monitoring. The goal of this study is to employ a linear-map convolutional neural network (CNN) to perform action recognition with RGB videos. To reduce the amount of the training data, the posture information is represented by skeleton data extracted from the 300 frames of one film. The two-stream method was applied to increase the accuracy of recognition by using the spatial and motion features of skeleton sequences. The relations of adjacent skeletal joints were employed to build the direct acyclic graph (DAG) matrices, source matrix, and target matrix. Two features were transferred by DAG matrices and expanded as color texture images. The linear-map CNN had a two-dimensional linear map at the beginning of each layer to adjust the number of channels. A two-dimensional CNN was used to recognize the actions. We applied the RGB videos from the action recognition datasets of the NTU RGB+D database, which was established by the Rapid-Rich Object Search Lab, to execute model training and performance evaluation. The experimental results show that the obtained precision, recall, specificity, F1-score, and accuracy were 86.9%, 86.1%, 99.9%, 86.3%, and 99.5%, respectively, in the cross-subject source, and 94.8%, 94.7%, 99.9%, 94.7%, and 99.9%, respectively, in the cross-view source. An important contribution of this work is that by using the skeleton sequences to produce the spatial and motion features and the DAG matrix to enhance the relation of adjacent skeletal joints, the computation speed was faster than the traditional schemes that utilize single frame image convolution. Therefore, this work exhibits the practical potential of real-life action recognition.
Tan-Hsu Tan; Jin-Hao Hus; Shing-Hong Liu; Yung-Fa Huang; Munkhjargal Gochoo. Using Direct Acyclic Graphs to Enhance Skeleton-Based Action Recognition with a Linear-Map Convolution Neural Network. Sensors 2021, 21, 3112 .
AMA StyleTan-Hsu Tan, Jin-Hao Hus, Shing-Hong Liu, Yung-Fa Huang, Munkhjargal Gochoo. Using Direct Acyclic Graphs to Enhance Skeleton-Based Action Recognition with a Linear-Map Convolution Neural Network. Sensors. 2021; 21 (9):3112.
Chicago/Turabian StyleTan-Hsu Tan; Jin-Hao Hus; Shing-Hong Liu; Yung-Fa Huang; Munkhjargal Gochoo. 2021. "Using Direct Acyclic Graphs to Enhance Skeleton-Based Action Recognition with a Linear-Map Convolution Neural Network." Sensors 21, no. 9: 3112.
Owing to progressive population aging, elderly people (aged 65 and above) face challenges in carrying out activities of daily living, while placement of the elderly in a care facility is expensive and mentally taxing for them. Thus, there is a need to develop their own homes into smart homes using new technologies. However, this raises concerns of privacy and data security for users since it can be handled remotely. Hence, with advancing technologies it is important to overcome this challenge using privacy-preserving and non-intrusive models. For this review, 235 articles were scanned from databases, out of which 31 articles pertaining to in-home technologies that assist the elderly in living independently were shortlisted for inclusion. They described the adoption of various methodologies like different sensor-based mechanisms, wearables, camera-based techniques, robots, and machine learning strategies to provide a safe and comfortable environment to the elderly. Recent innovations have rendered these technologies more unobtrusive and privacy-preserving with increasing use of environmental sensors and less use of cameras and other devices that may compromise the privacy of individuals. There is a need to develop a comprehensive system for smart homes which ensures patient safety, privacy, and data security; in addition, robots should be integrated with the existing sensor-based platforms to assist in carrying out daily activities and therapies as required.
Munkhjargal Gochoo; Fady Alnajjar; Tan-Hsu Tan; Sumayya Khalid. Towards Privacy-Preserved Aging in Place: A Systematic Review. Sensors 2021, 21, 3082 .
AMA StyleMunkhjargal Gochoo, Fady Alnajjar, Tan-Hsu Tan, Sumayya Khalid. Towards Privacy-Preserved Aging in Place: A Systematic Review. Sensors. 2021; 21 (9):3082.
Chicago/Turabian StyleMunkhjargal Gochoo; Fady Alnajjar; Tan-Hsu Tan; Sumayya Khalid. 2021. "Towards Privacy-Preserved Aging in Place: A Systematic Review." Sensors 21, no. 9: 3082.
Stroke, spinal cord injury and other neuromuscular disorders lead to impairments in the human body. Upper limb impairments, especially hand impairments affect activities of daily living (ADL) and reduce the quality of life. The purpose of this review is to compare and evaluate the available robotic rehabilitation and assistive devices that can lead to motor recovery or maintain the current motor functional level. A systematic review was conducted of the literature published in the years from 2016–2021, to focus on the most recent rehabilitation and assistive devices available in the market or research environments. A total of 230 studies published between 2016 and 2021 were identified from various databases. 107 were excluded with various reasons. Twenty-eight studies were taken into detailed review, to determine the efficacy of robotic devices in improving upper limb impairments or maintaining the current level from getting worse. It was concluded that with a good strategy and treatment plan; appropriate and regular use of these robotic rehabilitation and assistive devices do lead to improvements in current conditions of most of the subjects and prolonged use may lead to motor recovery.
Sumayya Khalid; Fady Alnajjar; Munkhjargal Gochoo; Abdulrahman Renawi; Shingo Shimoda. Robotic assistive and rehabilitation devices leading to motor recovery in upper limb: a systematic review. Disability and Rehabilitation: Assistive Technology 2021, 1 -15.
AMA StyleSumayya Khalid, Fady Alnajjar, Munkhjargal Gochoo, Abdulrahman Renawi, Shingo Shimoda. Robotic assistive and rehabilitation devices leading to motor recovery in upper limb: a systematic review. Disability and Rehabilitation: Assistive Technology. 2021; ():1-15.
Chicago/Turabian StyleSumayya Khalid; Fady Alnajjar; Munkhjargal Gochoo; Abdulrahman Renawi; Shingo Shimoda. 2021. "Robotic assistive and rehabilitation devices leading to motor recovery in upper limb: a systematic review." Disability and Rehabilitation: Assistive Technology , no. : 1-15.
Due to the constantly increasing demand for the automatic localization of landmarks in hand gesture recognition, there is a need for a more sustainable, intelligent, and reliable system for hand gesture recognition. The main purpose of this study was to develop an accurate hand gesture recognition system that is capable of error-free auto-landmark localization of any gesture dateable in an RGB image. In this paper, we propose a system based on landmark extraction from RGB images regardless of the environment. The extraction of gestures is performed via two methods, namely, fused and directional image methods. The fused method produced greater extracted gesture recognition accuracy. In the proposed system, hand gesture recognition (HGR) is done via several different methods, namely, (1) HGR via point-based features, which consist of (i) distance features, (ii) angular features, and (iii) geometric features; (2) HGR via full hand features, which are composed of (i) SONG mesh geometry and (ii) active model. To optimize these features, we applied gray wolf optimization. After optimization, a reweighted genetic algorithm was used for classification and gesture recognition. Experimentation was performed on five challenging datasets: Sign Word, Dexter1, Dexter + Object, STB, and NYU. Experimental results proved that auto landmark localization with the proposed feature extraction technique is an efficient approach towards developing a robust HGR system. The classification results of the reweighted genetic algorithm were compared with Artificial Neural Network (ANN) and decision tree. The developed system plays a significant role in healthcare muscle exercise.
Hira Ansar; Ahmad Jalal; Munkhjargal Gochoo; KiBum Kim. Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities. Sustainability 2021, 13, 2961 .
AMA StyleHira Ansar, Ahmad Jalal, Munkhjargal Gochoo, KiBum Kim. Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities. Sustainability. 2021; 13 (5):2961.
Chicago/Turabian StyleHira Ansar; Ahmad Jalal; Munkhjargal Gochoo; KiBum Kim. 2021. "Hand Gesture Recognition Based on Auto-Landmark Localization and Reweighted Genetic Algorithm for Healthcare Muscle Activities." Sustainability 13, no. 5: 2961.
This paper proposes a customized convolutional neural network for crack detection in concrete structures. The proposed method is compared to four existing deep learning methods based on training data size, data heterogeneity, network complexity, and the number of epochs. The performance of the proposed convolutional neural network (CNN) model is evaluated and compared to pretrained networks, i.e., the VGG-16, VGG-19, ResNet-50, and Inception V3 models, on eight datasets of different sizes, created from two public datasets. For each model, the evaluation considered computational time, crack localization results, and classification measures, e.g., accuracy, precision, recall, and F1-score. Experimental results demonstrated that training data size and heterogeneity among data samples significantly affect model performance. All models demonstrated promising performance on a limited number of diverse training data; however, increasing the training data size and reducing diversity reduced generalization performance, and led to overfitting. The proposed customized CNN and VGG-16 models outperformed the other methods in terms of classification, localization, and computational time on a small amount of data, and the results indicate that these two models demonstrate superior crack detection and localization for concrete structures.
Luqman Ali; Fady Alnajjar; Hamad Jassmi; Munkhjargal Gocho; Wasif Khan; M. Serhani. Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures. Sensors 2021, 21, 1688 .
AMA StyleLuqman Ali, Fady Alnajjar, Hamad Jassmi, Munkhjargal Gocho, Wasif Khan, M. Serhani. Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures. Sensors. 2021; 21 (5):1688.
Chicago/Turabian StyleLuqman Ali; Fady Alnajjar; Hamad Jassmi; Munkhjargal Gocho; Wasif Khan; M. Serhani. 2021. "Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures." Sensors 21, no. 5: 1688.
Advances in video capturing devices enable adaptive posture estimation (APE) and event classification of multiple human-based videos for smart systems. Accurate event classification and adaptive posture estimation are still challenging domains, although researchers work hard to find solutions. In this research article, we propose a novel method to classify stochastic remote sensing events and to perform adaptive posture estimation. We performed human silhouette extraction using the Gaussian Mixture Model (GMM) and saliency map. After that, we performed human body part detection and used a unified pseudo-2D stick model for adaptive posture estimation. Multifused data that include energy, 3D Cartesian view, angular geometric, skeleton zigzag and moveable body parts were applied. Using a charged system search, we optimized our feature vector and deep belief network. We classified complex events, which were performed over sports videos in the wild (SVW), Olympic sports, UCF aerial action dataset and UT-interaction datasets. The mean accuracy of human body part detection was 83.57% over the UT-interaction, 83.00% for the Olympic sports and 83.78% for the SVW dataset. The mean event classification accuracy was 91.67% over the UT-interaction, 92.50% for Olympic sports and 89.47% for SVW dataset. These results are superior compared to existing state-of-the-art methods.
Munkhjargal Gochoo; Israr Akhter; Ahmad Jalal; KiBum Kim. Stochastic Remote Sensing Event Classification over Adaptive Posture Estimation via Multifused Data and Deep Belief Network. Remote Sensing 2021, 13, 912 .
AMA StyleMunkhjargal Gochoo, Israr Akhter, Ahmad Jalal, KiBum Kim. Stochastic Remote Sensing Event Classification over Adaptive Posture Estimation via Multifused Data and Deep Belief Network. Remote Sensing. 2021; 13 (5):912.
Chicago/Turabian StyleMunkhjargal Gochoo; Israr Akhter; Ahmad Jalal; KiBum Kim. 2021. "Stochastic Remote Sensing Event Classification over Adaptive Posture Estimation via Multifused Data and Deep Belief Network." Remote Sensing 13, no. 5: 912.
The features and appearance of the human face are affected greatly by aging. A human face is an important aspect for human age identification from childhood through adulthood. Although many traits are used in human age estimation, this article discusses age classification using salient texture and facial landmark feature vectors. We propose a novel human age classification (HAC) model that can localize landmark points of the face. A robust multi-perspective view-based Active Shape Model (ASM) is generated and age classification is achieved using Convolution Neural Network (CNN). The HAC model is subdivided into the following steps: (1) at first, a face is detected using aYCbCr color segmentation model; (2) landmark localization is done on the face using a connected components approach and a ridge contour method; (3) an Active Shape Model (ASM) is generated on the face using three-sided polygon meshes and perpendicular bisection of a triangle; (4) feature extraction is achieved using anthropometric model, carnio-facial development, interior angle formulation, wrinkle detection and heat maps; (5) Sequential Forward Selection (SFS) is used to select the most ideal set of features; and (6) finally, the Convolution Neural Network (CNN) model is used to classify according to age in the correct age group. The proposed system outperforms existing statistical state-of-the-art HAC methods in terms of classification accuracy, achieving 91.58% with The Images of Groups dataset, 92.62% with the OUI Adience dataset and 94.59% with the FG-NET dataset. The system is applicable to many research areas including access control, surveillance monitoring, human–machine interaction and self-identification.
Syeda Rizwan; Ahmad Jalal; Munkhjargal Gochoo; KiBum Kim. Robust Active Shape Model via Hierarchical Feature Extraction with SFS-Optimized Convolution Neural Network for Invariant Human Age Classification. Electronics 2021, 10, 465 .
AMA StyleSyeda Rizwan, Ahmad Jalal, Munkhjargal Gochoo, KiBum Kim. Robust Active Shape Model via Hierarchical Feature Extraction with SFS-Optimized Convolution Neural Network for Invariant Human Age Classification. Electronics. 2021; 10 (4):465.
Chicago/Turabian StyleSyeda Rizwan; Ahmad Jalal; Munkhjargal Gochoo; KiBum Kim. 2021. "Robust Active Shape Model via Hierarchical Feature Extraction with SFS-Optimized Convolution Neural Network for Invariant Human Age Classification." Electronics 10, no. 4: 465.
The daily life-log routines of elderly individuals are susceptible to numerous complications in their physical healthcare patterns. Some of these complications can cause injuries, followed by extensive and expensive recovery stages. It is important to identify physical healthcare patterns that can describe and convey the exact state of an individual’s physical health while they perform their daily life activities. In this paper, we propose a novel Sustainable Physical Healthcare Pattern Recognition (SPHR) approach using a hybrid features model that is capable of distinguishing multiple physical activities based on a multiple wearable sensors system. Initially, we acquired raw data from well-known datasets, i.e., mobile health and human gait databases comprised of multiple human activities. The proposed strategy includes data pre-processing, hybrid feature detection, and feature-to-feature fusion and reduction, followed by codebook generation and classification, which can recognize sustainable physical healthcare patterns. Feature-to-feature fusion unites the cues from all of the sensors, and Gaussian mixture models are used for the codebook generation. For the classification, we recommend deep belief networks with restricted Boltzmann machines for five hidden layers. Finally, the results are compared with state-of-the-art techniques in order to demonstrate significant improvements in accuracy for physical healthcare pattern recognition. The experiments show that the proposed architecture attained improved accuracy rates for both datasets, and that it represents a significant sustainable physical healthcare pattern recognition (SPHR) approach. The anticipated system has potential for use in human–machine interaction domains such as continuous movement recognition, pattern-based surveillance, mobility assistance, and robot control systems.
Madiha Javeed; Munkhjargal Gochoo; Ahmad Jalal; KiBum Kim. HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks. Sustainability 2021, 13, 1699 .
AMA StyleMadiha Javeed, Munkhjargal Gochoo, Ahmad Jalal, KiBum Kim. HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks. Sustainability. 2021; 13 (4):1699.
Chicago/Turabian StyleMadiha Javeed; Munkhjargal Gochoo; Ahmad Jalal; KiBum Kim. 2021. "HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks." Sustainability 13, no. 4: 1699.
Due to the constantly increasing demand for automatic tracking and recognition systems, there is a need for more proficient, intelligent and sustainable human activity tracking. The main purpose of this study is to develop an accurate and sustainable human action tracking system that is capable of error-free identification of human movements irrespective of the environment in which those actions are performed. Therefore, in this paper we propose a stereoscopic Human Action Recognition (HAR) system based on the fusion of RGB (red, green, blue) and depth sensors. These sensors give an extra depth of information which enables the three-dimensional (3D) tracking of each and every movement performed by humans. Human actions are tracked according to four features, namely, (1) geodesic distance; (2) 3D Cartesian-plane features; (3) joints Motion Capture (MOCAP) features and (4) way-points trajectory generation. In order to represent these features in an optimized form, Particle Swarm Optimization (PSO) is applied. After optimization, a neuro-fuzzy classifier is used for classification and recognition. Extensive experimentation is performed on three challenging datasets: A Nanyang Technological University (NTU) RGB+D dataset; a UoL (University of Lincoln) 3D social activity dataset and a Collective Activity Dataset (CAD). Evaluation experiments on the proposed system proved that a fusion of vision sensors along with our unique features is an efficient approach towards developing a robust HAR system, having achieved a mean accuracy of 93.5% with the NTU RGB+D dataset, 92.2% with the UoL dataset and 89.6% with the Collective Activity dataset. The developed system can play a significant role in many computer vision-based applications, such as intelligent homes, offices and hospitals, and surveillance systems.
Nida Khalid; Munkhjargal Gochoo; Ahmad Jalal; KiBum Kim. Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System. Sustainability 2021, 13, 970 .
AMA StyleNida Khalid, Munkhjargal Gochoo, Ahmad Jalal, KiBum Kim. Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System. Sustainability. 2021; 13 (2):970.
Chicago/Turabian StyleNida Khalid; Munkhjargal Gochoo; Ahmad Jalal; KiBum Kim. 2021. "Modeling Two-Person Segmentation and Locomotion for Stereoscopic Action Identification: A Sustainable Video Surveillance System." Sustainability 13, no. 2: 970.
Foot Drop (FD) is a condition, which is very commonly found in post-stoke patients, however it can also be seen in patients with multiple sclerosis, and cerebral palsy. It is a sign of neuromuscular damage caused by the weakness of the muscles. There are various approaches of FD’s rehabilitation, such as physiotherapy, surgery, and the use of technological devices. Recently, researchers have worked on developing various technologies to enhance assisting and rehabilitation of FD. This review analyzes different types of technologies available for the rehabilitation of FD. This include devices that are available commercially, as well as, the proposed studies. 101 studies published between 2015 and 2020 were identified for the review, many were excluded due to various reasons, e.g., they were not robot-based devices, did not include foot drop as one of the targeted diseases, or was insufficient information. 24 studies that met our inclusion criteria, were assessed. These studies were further classified into two different categories: robot-based ankle-foot orthosis (RAFO) and Functional Electrical Stimulation (FES) devices. Studies included showed that both RAFO and FES showed considerable improvement in the gait cycle of the patients. Future trends are inclining towards FES and muscle synergies for further developments.
Fady Alnajjar; Riadh Zaier; Sumayya Khalid; Munkhjargal Gochoo. Trends and Technologies in Rehabilitation of Foot Drop: A Systematic Review. Expert Review of Medical Devices 2020, 18, 31 -46.
AMA StyleFady Alnajjar, Riadh Zaier, Sumayya Khalid, Munkhjargal Gochoo. Trends and Technologies in Rehabilitation of Foot Drop: A Systematic Review. Expert Review of Medical Devices. 2020; 18 (1):31-46.
Chicago/Turabian StyleFady Alnajjar; Riadh Zaier; Sumayya Khalid; Munkhjargal Gochoo. 2020. "Trends and Technologies in Rehabilitation of Foot Drop: A Systematic Review." Expert Review of Medical Devices 18, no. 1: 31-46.
Current environmental concerns have led to a search of more environmentally friendly manufacturing methods; thus, natural fibers have gained attention in the 3D printing industry to be used as bio-filters along with thermoplastics. The utilization of natural fibers is very convenient as they are easily available, cost-effective, eco-friendly, and biodegradable. Using natural fibers rather than synthetic fibers in the production of the 3D printing filaments will reduce gas emissions associated with the production of the synthetic fibers that would add to the current pollution problem. As a matter of fact, natural fibers have a reinforcing effect on plastics. This review analyzes how the properties of the different polymers vary when natural fibers processed to produce filaments for 3D Printing are added. The results of using natural fibers for 3D Printing are presented in this study and appeared to be satisfactory, while a few studies have reported some issues.
Waleed Ahmed; Fady Alnajjar; Essam Zaneldin; Ali H. Al-Marzouqi; Munkhjargal Gochoo; Sumayya Khalid. Implementing FDM 3D Printing Strategies Using Natural Fibers to Produce Biomass Composite. Materials 2020, 13, 4065 .
AMA StyleWaleed Ahmed, Fady Alnajjar, Essam Zaneldin, Ali H. Al-Marzouqi, Munkhjargal Gochoo, Sumayya Khalid. Implementing FDM 3D Printing Strategies Using Natural Fibers to Produce Biomass Composite. Materials. 2020; 13 (18):4065.
Chicago/Turabian StyleWaleed Ahmed; Fady Alnajjar; Essam Zaneldin; Ali H. Al-Marzouqi; Munkhjargal Gochoo; Sumayya Khalid. 2020. "Implementing FDM 3D Printing Strategies Using Natural Fibers to Produce Biomass Composite." Materials 13, no. 18: 4065.
Alistair A. Vogan; Fady Alnajjar; Munkhjargal Gochoo; Sumayya Khalid. Robots, AI, and Cognitive Training in an Era of Mass Age-Related Cognitive Decline: A Systematic Review. IEEE Access 2020, 8, 18284 -18304.
AMA StyleAlistair A. Vogan, Fady Alnajjar, Munkhjargal Gochoo, Sumayya Khalid. Robots, AI, and Cognitive Training in an Era of Mass Age-Related Cognitive Decline: A Systematic Review. IEEE Access. 2020; 8 ():18284-18304.
Chicago/Turabian StyleAlistair A. Vogan; Fady Alnajjar; Munkhjargal Gochoo; Sumayya Khalid. 2020. "Robots, AI, and Cognitive Training in an Era of Mass Age-Related Cognitive Decline: A Systematic Review." IEEE Access 8, no. : 18284-18304.
State-of-the-art (SoTA) models have improved the accuracy of object detection with a large margin via a FP (feature pyramid). FP is a top-down aggregation to collect semantically strong features to improve scale invariance in both two-stage and one-stage detectors. However, this top-down pathway cannot preserve accurate object positions due to the shift-effect of pooling. Thus, the advantage of FP to improve detection accuracy will disappear when more layers are used. The original FP lacks a bottom-up pathway to offset the lost information from lower-layer feature maps. It performs well in large-sized object detection but poor in small-sized object detection. A new structure "residual feature pyramid" is proposed in this paper. It is bidirectional to fuse both deep and shallow features towards more effective and robust detection for both small-sized and large-sized objects. Due to the "residual" nature, it can be easily trained and integrated to different backbones (even deeper or lighter) than other bi-directional methods. One important property of this residual FP is: accuracy improvement is still found even if more layers are adopted. Extensive experiments on VOC and MS COCO datasets showed the proposed method achieved the SoTA results for highly-accurate and efficient object detection..
Ping-Yang Chen; Jun-Wei Hsieh; Chien-Yao Wang; Mark Hong-Yuan Liao; Munkhjargal Gochoo. Residual Bi-Fusion Feature Pyramid Network for Accurate Single-shot Object Detection. 2019, 1 .
AMA StylePing-Yang Chen, Jun-Wei Hsieh, Chien-Yao Wang, Mark Hong-Yuan Liao, Munkhjargal Gochoo. Residual Bi-Fusion Feature Pyramid Network for Accurate Single-shot Object Detection. . 2019; ():1.
Chicago/Turabian StylePing-Yang Chen; Jun-Wei Hsieh; Chien-Yao Wang; Mark Hong-Yuan Liao; Munkhjargal Gochoo. 2019. "Residual Bi-Fusion Feature Pyramid Network for Accurate Single-shot Object Detection." , no. : 1.