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Impact of Wireless Sensor Data Mining with Hybrid Deep Learning for Human Activity Recognition

Rajit Nair, Mahmoud Ragab, Osama A. Mujallid, Khadijah Ahmad Mohammad, Romany F. Mansour, G. K. Viju, Shalli Rani
2022 Wireless Communications and Mobile Computing  
This work aims to demonstrate how a hybrid deep learning model may be used to recognize human behavior.  ...  Deep learning methodologies such as convolutional neural networks and recurrent neural networks will extract the features and achieve the classification goal.  ...  The authors, therefore, acknowledge with thanks DSR for technical and financial support.  ... 
doi:10.1155/2022/9457536 fatcat:luntucqth5frpdjgggkz4bialy

Human action recognition using transfer learning with deep representations

Allah Bux Sargano, Xiaofeng Wang, Plamen Angelov, Zulfiqar Habib
2017 2017 International Joint Conference on Neural Networks (IJCNN)  
This research work presents an innovative method for human action recognition using pre-trained Convolutional Neural Networks (CNNs) model as a source architecture for extracting features from the target  ...  dataset, followed by a hybrid Support Vector Machines and K-Nearest Neighbor (SVM-KNN) classifier for action classification.  ...  For addressing this issue, [37] combined the deep convolutional networks with trajectory for action recognition.  ... 
doi:10.1109/ijcnn.2017.7965890 dblp:conf/ijcnn/SarganoWAH17 fatcat:pxqdjr3lbfctlezzgc7zbf73lu

A Hybrid Deep Model Using Deep Learning and Dense Optical Flow Approaches for Human Activity Recognition

Senem Tanberk, Zeynep Hilal Kilimci, Dilek Tukel, Mitat Uysal, Selim Akyokus
2020 IEEE Access  
In this study, we propose a hybrid deep model to understand and interpret videos focusing on human activity recognition.  ...  information over video frames for the purpose of human activity recognition.  ...  Deep neural network models are also now being also proposed for human activity recognition field.  ... 
doi:10.1109/access.2020.2968529 fatcat:bafvat24ondflmzm454xc356eu

Video Processing using Deep learning Techniques: A Systematic Literature Review

Vijeta Sharma, Manjari Gupta, Ajai Kumar, Deepti Mishra
2021 IEEE Access  
The prominent fields of video processing research are observed as human action recognition, crowd anomaly detection, and behavior analysis.  ...  We categorize the deep learning technique for video processing as CNN, DNN, and RNN based.  ...  The authors of [93] introduce temporal segment networks for human action recognition.  ... 
doi:10.1109/access.2021.3118541 fatcat:oadlu4uyirc2tanqrixz3sn6ny

A Deep Learning Approach for Safety Monitoring of Sick People

GP Ramesh
2020 Bioscience Biotechnology Research Communications  
This research proposed novel Convolutional Neural Network (ConvNet/CNN) to predict the action based on human activity.  ...  This project illustrates a method of smart recognition of human behavior to automatically recognize human actions from skeletal joint movements and integrate the skills.  ...  Deep Learning Process: In several functions, from object detection and other synthesis model recognition, a deep neural network offers state-of-the-art precision. deep model will learn based on previous  ... 
doi:10.21786/bbrc/13.13/55 fatcat:o3apo2wirjb2fnkjprf3z5tjgi

Multi Modal RGB D Action Recognition with CNN LSTM Ensemble Deep Network

D. Srihari, P. V.
2020 International Journal of Advanced Computer Science and Applications  
The objective of this work is to perform multi modal human action recognition on an ensemble hybrid network of CNN and LSTM layers.  ...  Human action recognition has transformed from a video processing problem into multi modal machine learning problem.  ...  Therefore, the hybrid combination of CNN and LSTMs is the most widely applied model for human action recognition because of their abilities to decode spatial and temporal information simultaneously [27  ... 
doi:10.14569/ijacsa.2020.0111284 fatcat:h63esrv6pfhljkzt7xdy6ygypa

Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition [article]

Xikun Zhang, Chang Xu, Xinmei Tian, Dacheng Tao
2018 arXiv   pre-print
A graph edge convolutional neural network is then designed for skeleton based action recognition.  ...  This paper investigates body bones from skeleton data for skeleton based action recognition.  ...  In this paper, we revisit skeletal data from the perspective of bones, and propose an edge convolutional neural network for action recognition.  ... 
arXiv:1805.06184v2 fatcat:vzmdrivyqjg6tddi4obigir3t4

Analysis of Deep Neural Networks For Human Activity Recognition in Videos – A Systematic Literature Review

Hadiqa Aman Ullah, Sukumar Letchmunan, M. Sultan Zia, Umair Muneer Butt, Fadratul Hafinaz Hassan
2021 IEEE Access  
The scope of this study is to explore deep neural networks for human activity recognition. Upon exploring 70 selected studies, we categorize deep neural architecture into eight types.  ...  current frame. • For using deep learning for human activity recognition, deep neural networks possess a large number of parameters with high computational costs to process video data.  ... 
doi:10.1109/access.2021.3110610 fatcat:ussooxm7azfljpb5prsm7creaa

Classifying and Visualizing Motion Capture Sequences using Deep Neural Networks [article]

Kyunghyun Cho, Xi Chen
2014 arXiv   pre-print
In this paper, we propose a novel system to recognize the actions from skeleton data with simple, but effective, features using deep neural networks.  ...  We use deep autoencoder to visualize learnt features, and the experiments show that deep neural networks can capture more discriminative information than, for instance, principal component analysis can  ...  DEEP NEURAL NETWORKS: MULTI-LAYER PERCEPTRONS A multi-layer perceptron (MLP) is a type of deep neural networks that is able to perform classification (see, e.g., (Haykin, 2009) ).  ... 
arXiv:1306.3874v2 fatcat:gyeibuyvhbdklde3zrw2jjd47m

A Variational Graph Autoencoder for Manipulation Action Recognition and Prediction [article]

Gamze Akyol, Sanem Sariel, Eren Erdal Aksoy
2021 arXiv   pre-print
Recognition and prediction of observed human manipulation actions have their roots in the applications related to, for instance, human-robot interaction and robot learning from demonstration.  ...  Our network has a variational autoencoder structure with two branches: one for identifying the input graph type and one for predicting the future graphs.  ...  In this manner, our work falls into the early action prediction category. In [26] , a hybrid deep neural network is used for the action prediction.  ... 
arXiv:2110.13280v1 fatcat:6ywfdrextrc3bd32jjvsdhyqbm

Hybridized Hierarchical Deep Convolutional Neural Network for Sports Rehabilitation Exercises

Dapeng Tang
2020 IEEE Access  
In this article, Hybridized Hierarchical Deep Convolutional Neural Network (HHDCNN) has been introduced to enhance the accuracy, image segmentation of sports athletics exercise rehabilitation.  ...  INDEX TERMS Sports rehabilitation, deep convolutional neural network, segmentation. VOLUME 8, 2020 This work is licensed under a Creative Commons Attribution 4.0 License.  ...  The hierarchical hybridized deep convolutional network (HHDCNN) which manages action recognition and motion prediction in accordance to collect action patterns using graph-based operations.  ... 
doi:10.1109/access.2020.3005189 fatcat:eg6c5p7lczhvlnuymzpichqxme

Human Activity Recognition via Hybrid Deep Learning Based Model

Imran Ullah Khan, Sitara Afzal, Jong Weon Lee
2022 Sensors  
Considering these limitations, we develop a hybrid model by incorporating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for activity recognition where CNN is used for spatial features  ...  An extensive ablation study is performed over different traditional machine learning and deep learning models to obtain the optimum solution for HAR.  ...  They presented a symbiotic neural network with a backbone, action recognition head, and motion prediction head. These two heads are connected and improve the joint recognitions.  ... 
doi:10.3390/s22010323 pmid:35009865 pmcid:PMC8749555 fatcat:3zrdns3pcfecvpxgmdsngdsgmm

New Hybrid Deep Learning Method to Recognize Human Action from Video

Md Shofiqul Islam, Sunjida Sultana, Md Jabbarul Islam
2021 Jurnal Ilmiah Teknik Elektro Komputer dan Informatika  
The use of deep neural networks to recognize human behavior has become a popular issue in recent years.  ...  Model loss Vol. 7, No. 2, August 2021, pp. 306-313 New Hybrid Deep Learning Method to Recognize Human Action from Video (Md ShofiqulIslam) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI  ...  The model uses a fully connected network (DenseNet) extension, with time information added to all convolutional and pooling layers, as well as a hierarchical spatial structure.  ... 
doi:10.26555/jiteki.v7i2.21499 fatcat:huva5lbeobelhnzrju7fx6ceoy

Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning Techniques

Huaijun Wang, Jing Zhao, Junhuai Li, Ling Tian, Pengjia Tu, Ting Cao, Yang An, Kan Wang, Shancang Li
2020 Security and Communication Networks  
In this work, we first build a deep convolutional neural network (CNN) for extracting features from the data collected by sensors.  ...  Many existing techniques, such as deep learning, have been developed for specific activity recognition, but little for the recognition of the transitions between activities.  ...  Kuang [35] applied BLSTM to construct the behavior recognition model. Hassan et al. [36] used deep belief network (DBN) for human behavior recognition.  ... 
doi:10.1155/2020/2132138 fatcat:wfvdnsogavbr7pbue3wogjwy5m

Privacy preserving human activity recognition framework using an optimized prediction algorithm

Kambala Vijaya Kumar, Jonnadula Harikiran
2022 IAES International Journal of Artificial Intelligence (IJ-AI)  
To address this problem, we proposed an algorithm known optimized prediction algorithm for privacy preserving activity recognition (OPA-PPAR) based on deep neural networks.  ...  The privacy model enhances the privacy of humans while permitting highly accurate approach towards action recognition.  ...  [6] proposed a cloud-based service to achieve privacy preserving action recognition using deep convolution neural network (CNN) model.  ... 
doi:10.11591/ijai.v11.i1.pp254-264 fatcat:2z7r3ppyq5gyfiz7zxkaecnata
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