Human activity recognition is a significant area of research in artificial intelligence for surveillance,healthcare,sports,and human-computer interaction applications.The article benchmarks the performance of You Only...Human activity recognition is a significant area of research in artificial intelligence for surveillance,healthcare,sports,and human-computer interaction applications.The article benchmarks the performance of You Only Look Once version 11-based(YOLOv11-based)architecture for multi-class human activity recognition.The article benchmarks the performance of You Only Look Once version 11-based(YOLOv11-based)architecture for multi-class human activity recognition.The dataset consists of 14,186 images across 19 activity classes,from dynamic activities such as running and swimming to static activities such as sitting and sleeping.Preprocessing included resizing all images to 512512 pixels,annotating them in YOLO’s bounding box format,and applying data augmentation methods such as flipping,rotation,and cropping to enhance model generalization.The proposed model was trained for 100 epochs with adaptive learning rate methods and hyperparameter optimization for performance improvement,with a mAP@0.5 of 74.93%and a mAP@0.5-0.95 of 64.11%,outperforming previous versions of YOLO(v10,v9,and v8)and general-purpose architectures like ResNet50 and EfficientNet.It exhibited improved precision and recall for all activity classes with high precision values of 0.76 for running,0.79 for swimming,0.80 for sitting,and 0.81 for sleeping,and was tested for real-time deployment with an inference time of 8.9 ms per image,being computationally light.Proposed YOLOv11’s improvements are attributed to architectural advancements like a more complex feature extraction process,better attention modules,and an anchor-free detection mechanism.While YOLOv10 was extremely stable in static activity recognition,YOLOv9 performed well in dynamic environments but suffered from overfitting,and YOLOv8,while being a decent baseline,failed to differentiate between overlapping static activities.The experimental results determine proposed YOLOv11 to be the most appropriate model,providing an ideal balance between accuracy,computational efficiency,and robustness for real-world deployment.Nevertheless,there exist certain issues to be addressed,particularly in discriminating against visually similar activities and the use of publicly available datasets.Future research will entail the inclusion of 3D data and multimodal sensor inputs,such as depth and motion information,for enhancing recognition accuracy and generalizability to challenging real-world environments.展开更多
Automated recognition of violent activities from videos is vital for public safety,but often raises significant privacy concerns due to the sensitive nature of the footage.Moreover,resource constraints often hinder th...Automated recognition of violent activities from videos is vital for public safety,but often raises significant privacy concerns due to the sensitive nature of the footage.Moreover,resource constraints often hinder the deployment of deep learning-based complex video classification models on edge devices.With this motivation,this study aims to investigate an effective violent activity classifier while minimizing computational complexity,attaining competitive performance,and mitigating user data privacy concerns.We present a lightweight deep learning architecture with fewer parameters for efficient violent activity recognition.We utilize a two-stream formation of 3D depthwise separable convolution coupled with a linear self-attention mechanism for effective feature extraction,incorporating federated learning to address data privacy concerns.Experimental findings demonstrate the model’s effectiveness with test accuracies from 96%to above 97%on multiple datasets by incorporating the FedProx aggregation strategy.These findings underscore the potential to develop secure,efficient,and reliable solutions for violent activity recognition in real-world scenarios.展开更多
Activity recognition is a challenging topic in the field of computer vision that has various applications,including surveillance systems,industrial automation,and human-computer interaction.Today,the demand for automa...Activity recognition is a challenging topic in the field of computer vision that has various applications,including surveillance systems,industrial automation,and human-computer interaction.Today,the demand for automation has greatly increased across industries worldwide.Real-time detection requires edge devices with limited computational time.This study proposes a novel hybrid deep learning system for human activity recognition(HAR),aiming to enhance the recognition accuracy and reduce the computational time.The proposed system combines a pretrained image classification model with a sequence analysis model.First,the dataset was divided into a training set(70%),validation set(10%),and test set(20%).Second,all the videos were converted into frames and deep-based features were extracted from each frame using convolutional neural networks(CNNs)with a vision transformer.Following that,bidirectional long short-term memory(BiLSTM)-and temporal convolutional network(TCN)-based models were trained using the training set,and their performances were evaluated using the validation set and test set.Four benchmark datasets(UCF11,UCF50,UCF101,and JHMDB)were used to evaluate the performance of the proposed HAR-based system.The experimental results showed that the combination of ConvNeXt and the TCN-based model achieved a recognition accuracy of 97.73%for UCF11,98.81%for UCF50,98.46%for UCF101,and 83.38%for JHMDB,respectively.This represents improvements in the recognition accuracy of 4%,2.67%,3.67%,and 7.08%for the UCF11,UCF50,UCF101,and JHMDB datasets,respectively,over existing models.Moreover,the proposed HAR-based system obtained superior recognition accuracy,shorter computational times,and minimal memory usage compared to the existing models.展开更多
This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities,and proposes an optimized ontology framework to im...This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities,and proposes an optimized ontology framework to improve recognition accuracy and computational efficiency.The method in this paper adopts the event sequence segmentation technique,combines location awareness with time interval reasoning,and improves human activity recognition through ontology reasoning.Compared with the existing methods,the framework performs better when dealing with uncertain data and complex scenes,and the experimental results show that its recognition accuracy is improved by 15.6%and processing time is reduced by 22.4%.In addition,it is found that with the increase of context complexity,the traditional ontology inferencemodel has limitations in abnormal behavior recognition,especially in the case of high data redundancy,which tends to lead to a decrease in recognition accuracy.This study effectively mitigates this problem by optimizing the ontology matching algorithm and combining parallel computing and deep learning techniques to enhance the activity recognition capability in complex environments.展开更多
This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, ...This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, MotionSense, and PAMAP2—to develop a generalized MLP model for classifying six human activities. Performance analysis of the fused model for each dataset reveals accuracy rates of 95.83 for WISDM, 97 for DaLiAc, 94.65 for MotionSense, and 98.54 for PAMAP2. A comparative evaluation was conducted between the fused MLP model and the individual dataset models, with the latter tested on separate validation sets. The results indicate that the MLP model, trained on the fused dataset, exhibits superior performance relative to the models trained on individual datasets. This finding suggests that multisource data fusion significantly enhances the generalization and accuracy of HAR systems. The improved performance underscores the potential of integrating diverse data sources to create more robust and comprehensive models for activity recognition.展开更多
Human activity recognition is commonly used in several Internet of Things applications to recognize different contexts and respond to them.Deep learning has gained momentum for identifying activities through sensors,s...Human activity recognition is commonly used in several Internet of Things applications to recognize different contexts and respond to them.Deep learning has gained momentum for identifying activities through sensors,smartphones or even surveillance cameras.However,it is often difficult to train deep learning models on constrained IoT devices.The focus of this paper is to propose an alternative model by constructing a Deep Learning-based Human Activity Recognition framework for edge computing,which we call DL-HAR.The goal of this framework is to exploit the capabilities of cloud computing to train a deep learning model and deploy it on less-powerful edge devices for recognition.The idea is to conduct the training of the model in the Cloud and distribute it to the edge nodes.We demonstrate how the DL-HAR can perform human activity recognition at the edge while improving efficiency and accuracy.In order to evaluate the proposed framework,we conducted a comprehensive set of experiments to validate the applicability of DL-HAR.Experimental results on the benchmark dataset show a significant increase in performance compared with the state-of-the-art models.展开更多
Human group activity recognition(GAR)has attracted significant attention from computer vision researchers due to its wide practical applications in security surveillance,social role understanding and sports video anal...Human group activity recognition(GAR)has attracted significant attention from computer vision researchers due to its wide practical applications in security surveillance,social role understanding and sports video analysis.In this paper,we give a comprehensive overview of the advances in group activity recognition in videos during the past 20 years.First,we provide a summary and comparison of 11 GAR video datasets in this field.Second,we survey the group activity recognition methods,including those based on handcrafted features and those based on deep learning networks.For better understanding of the pros and cons of these methods,we compare various models from the past to the present.Finally,we outline several challenging issues and possible directions for future research.From this comprehensive literature review,readers can obtain an overview of progress in group activity recognition for future studies.展开更多
With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and compute...With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation.However,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing data.It is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational complexity.Visual Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images.The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT.In this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved.By diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is mitigated.By fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing images.The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.展开更多
Human activity tracking plays a vital role in human–computer interaction.Traditional human activity recognition(HAR)methods adopt special devices,such as cameras and sensors,to track both macro-and micro-activities.R...Human activity tracking plays a vital role in human–computer interaction.Traditional human activity recognition(HAR)methods adopt special devices,such as cameras and sensors,to track both macro-and micro-activities.Recently,wireless signals have been exploited to track human motion and activities in indoor environments without additional equipment.This study proposes a device-free WiFi-based micro-activity recognition method that leverages the channel state information(CSI)of wireless signals.Different from existed CSI-based microactivity recognition methods,the proposed method extracts both amplitude and phase information from CSI,thereby providing more information and increasing detection accuracy.The proposed method harnesses an effective signal processing technique to reveal the unique patterns of each activity.We applied a machine learning algorithm to recognize the proposed micro-activities.The proposed method has been evaluated in both line of sight(LOS)and none line of sight(NLOS)scenarios,and the empirical results demonstrate the effectiveness of the proposed method with several users.展开更多
This paper proposes a hybrid approach for recognizing human activities from trajectories. First, an improved hidden Markov model （HMM） parameter learning algorithm, HMM-PSO, is proposed, which achieves a better bala...This paper proposes a hybrid approach for recognizing human activities from trajectories. First, an improved hidden Markov model （HMM） parameter learning algorithm, HMM-PSO, is proposed, which achieves a better balance between the global and local exploitation by the nonlinear update strategy and repulsion operation. Then, the event probability sequence （EPS） which consists of a series of events is computed to describe the unique characteristic of human activities. The anatysis on EPS indicates that it is robust to the changes in viewing direction and contributes to improving the recognition rate. Finally, the effectiveness of the proposed approach is evaluated by data experiments on current popular datasets.展开更多
Human Activity Recognition(HAR)is an active research area due to its applications in pervasive computing,human-computer interaction,artificial intelligence,health care,and social sciences.Moreover,dynamic environments...Human Activity Recognition(HAR)is an active research area due to its applications in pervasive computing,human-computer interaction,artificial intelligence,health care,and social sciences.Moreover,dynamic environments and anthropometric differences between individuals make it harder to recognize actions.This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications.It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network.Moreover,the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information.Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction.For temporal sequence,this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short TermMemory(BiLSTM)to capture longtermdependencies.Two state-of-the-art datasets,UCF101 and HMDB51,are used for evaluation purposes.In addition,seven state-of-the-art optimizers are used to fine-tune the proposed network parameters.Furthermore,this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network(CNN),where two streams use RGB data.In contrast,the other uses optical flow images.Finally,the proposed ensemble approach using max hard voting outperforms state-ofthe-art methods with 96.30%and 90.07%accuracies on the UCF101 and HMDB51 datasets.展开更多
Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports detection.We have developed a real-time hand gesture recognition system using inertialsens...Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports detection.We have developed a real-time hand gesture recognition system using inertialsensors for the smart home application. Developing such a model facilitatesthe medical health field (elders or disabled ones). Home automation has alsobeen proven to be a tremendous benefit for the elderly and disabled. Residentsare admitted to smart homes for comfort, luxury, improved quality of life,and protection against intrusion and burglars. This paper proposes a novelsystem that uses principal component analysis, linear discrimination analysisfeature extraction, and random forest as a classifier to improveHGRaccuracy.We have achieved an accuracy of 94% over the publicly benchmarked HGRdataset. The proposed system can be used to detect hand gestures in thehealthcare industry as well as in the industrial and educational sectors.展开更多
A new method for complex activity recognition in videos by key frames was presented. The progressive bisection strategy(PBS) was employed to divide the complex activity into a series of simple activities and the key f...A new method for complex activity recognition in videos by key frames was presented. The progressive bisection strategy(PBS) was employed to divide the complex activity into a series of simple activities and the key frames representing the simple activities were extracted by the self-splitting competitive learning(SSCL) algorithm. A new similarity criterion of complex activities was defined. Besides the regular visual factor, the order factor and the interference factor measuring the timing matching relationship of the simple activities and the discontinuous matching relationship of the simple activities respectively were considered. On these bases, the complex human activity recognition could be achieved by calculating their similarities. The recognition error was reduced compared with other methods when ignoring the recognition of simple activities. The proposed method was tested and evaluated on the self-built broadcast gymnastic database and the dancing database. The experimental results prove the superior efficiency.展开更多
We study the problem of humanactivity recognition from RGB-Depth(RGBD)sensors when the skeletons are not available.The skeleton tracking in Kinect SDK workswell when the human subject is facing thecamera and there are...We study the problem of humanactivity recognition from RGB-Depth(RGBD)sensors when the skeletons are not available.The skeleton tracking in Kinect SDK workswell when the human subject is facing thecamera and there are no occlusions.In surveillance or nursing home monitoring scenarios,however,the camera is usually mounted higher than human subjects,and there may beocclusions.The interest-point based approachis widely used in RGB based activity recognition,it can be used in both RGB and depthchannels.Whether we should extract interestpoints independently of each channel or extract interest points from only one of thechannels is discussed in this paper.The goal ofthis paper is to compare the performances ofdifferent methods of extracting interest points.In addition,we have developed a depth mapbased descriptor and built an RGBD dataset,called RGBD-SAR,for senior activity recognition.We show that the best performance isachieved when we extract interest points solely from RGB channels,and combine the RGBbased descriptors with the depth map-baseddescriptors.We also present a baseline performance of the RGBD-SAR dataset.展开更多
Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,...Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and entertainment.Since wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor values.Further,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,etc.Recently-developed Artificial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR process.With this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare environment.The proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare service.In addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the out-liers.IHPTDL-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of users.Moreover,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN model.Finally,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different aspects.The experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures.展开更多
Activity and motion recognition using Wi-Fi signals,mainly channel state information(CSI),has captured the interest of many researchers in recent years.Many research studies have achieved splendid results with the hel...Activity and motion recognition using Wi-Fi signals,mainly channel state information(CSI),has captured the interest of many researchers in recent years.Many research studies have achieved splendid results with the help of machine learning models from different applications such as healthcare services,sign language translation,security,context awareness,and the internet of things.Nevertheless,most of these adopted studies have some shortcomings in the machine learning algorithms as they rely on recurrence and convolutions and,thus,precluding smooth sequential computation.Therefore,in this paper,we propose a deep-learning approach based solely on attention,i.e.,the sole Self-Attention Mechanism model(Sole-SAM),for activity and motion recognition using Wi-Fi signals.The Sole-SAM was deployed to learn the features representing different activities and motions from the raw CSI data.Experiments were carried out to evaluate the performance of the proposed Sole-SAM architecture.The experimental results indicated that our proposed system took significantly less time to train than models that rely on recurrence and convolutions like Long Short-Term Memory(LSTM)and Recurrent Neural Network(RNN).Sole-SAM archived a 0.94%accuracy level,which is 0.04%better than RNN and 0.02%better than LSTM.展开更多
With the rapid advancement of wearable devices,Human Activities Recognition(HAR)based on these devices has emerged as a prominent research field.The objective of this study is to enhance the recognition performance of...With the rapid advancement of wearable devices,Human Activities Recognition(HAR)based on these devices has emerged as a prominent research field.The objective of this study is to enhance the recognition performance of HAR by proposing an LSTM-1DCNN recognition algorithm that utilizes a single triaxial accelerometer.This algorithm comprises two branches:one branch consists of a Long and Short-Term Memory Network(LSTM),while the other parallel branch incorporates a one-dimensional Convolutional Neural Network(1DCNN).The parallel architecture of LSTM-1DCNN initially extracts spatial and temporal features from the accelerometer data separately,which are then concatenated and fed into a fully connected neural network for information fusion.In the LSTM-1DCNN architecture,the 1DCNN branch primarily focuses on extracting spatial features during convolution operations,whereas the LSTM branch mainly captures temporal features.Nine sets of accelerometer data from five publicly available HAR datasets are employed for training and evaluation purposes.The performance of the proposed LSTM-1DCNN model is compared with five other HAR algorithms including Decision Tree,Random Forest,Support Vector Machine,1DCNN,and LSTM on these five public datasets.Experimental results demonstrate that the F1-score achieved by the proposed LSTM-1DCNN ranges from 90.36%to 99.68%,with a mean value of 96.22%and standard deviation of 0.03 across all evaluated metrics on these five public datasets-outperforming other existing HAR algorithms significantly in terms of evaluation metrics used in this study.Finally the proposed LSTM-1DCNN is validated in real-world applications by collecting acceleration data of seven human activities for training and testing purposes.Subsequently,the trained HAR algorithm is deployed on Android phones to evaluate its performance.Experimental results demonstrate that the proposed LSTM-1DCNN algorithm achieves an impressive F1-score of 97.67%on our self-built dataset.In conclusion,the fusion of temporal and spatial information in the measured data contributes to the excellent HAR performance and robustness exhibited by the proposed 1DCNN-LSTM architecture.展开更多
Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study intr...Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.展开更多
The purpose of Human Activities Recognition(HAR)is to recognize human activities with sensors like accelerometers and gyroscopes.The normal research strategy is to obtain better HAR results by finding more efficient e...The purpose of Human Activities Recognition(HAR)is to recognize human activities with sensors like accelerometers and gyroscopes.The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms.In this paper,we experimentally validate the HAR process and its various algorithms independently.On the base of which,it is further proposed that,in addition to the necessary eigenvalues and intelligent algorithms,correct prior knowledge is even more critical.The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object,the sampling process,the sampling data,the HAR algorithm,etc.Thus,a solution is presented under the guidance of right prior knowledge,using Back-Propagation neural networks(BP networks)and simple Convolutional Neural Networks(CNN).The results show that HAR can be achieved with 90%–100%accuracy.Further analysis shows that intelligent algorithms for pattern recognition and classification problems,typically represented by HAR,require correct prior knowledge to work effectively.展开更多
In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health infor...In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.展开更多
基金supported by King Saud University,Riyadh,Saudi Arabia,under Ongoing Research Funding Program(ORF-2025-951).
文摘Human activity recognition is a significant area of research in artificial intelligence for surveillance,healthcare,sports,and human-computer interaction applications.The article benchmarks the performance of You Only Look Once version 11-based(YOLOv11-based)architecture for multi-class human activity recognition.The article benchmarks the performance of You Only Look Once version 11-based(YOLOv11-based)architecture for multi-class human activity recognition.The dataset consists of 14,186 images across 19 activity classes,from dynamic activities such as running and swimming to static activities such as sitting and sleeping.Preprocessing included resizing all images to 512512 pixels,annotating them in YOLO’s bounding box format,and applying data augmentation methods such as flipping,rotation,and cropping to enhance model generalization.The proposed model was trained for 100 epochs with adaptive learning rate methods and hyperparameter optimization for performance improvement,with a mAP@0.5 of 74.93%and a mAP@0.5-0.95 of 64.11%,outperforming previous versions of YOLO(v10,v9,and v8)and general-purpose architectures like ResNet50 and EfficientNet.It exhibited improved precision and recall for all activity classes with high precision values of 0.76 for running,0.79 for swimming,0.80 for sitting,and 0.81 for sleeping,and was tested for real-time deployment with an inference time of 8.9 ms per image,being computationally light.Proposed YOLOv11’s improvements are attributed to architectural advancements like a more complex feature extraction process,better attention modules,and an anchor-free detection mechanism.While YOLOv10 was extremely stable in static activity recognition,YOLOv9 performed well in dynamic environments but suffered from overfitting,and YOLOv8,while being a decent baseline,failed to differentiate between overlapping static activities.The experimental results determine proposed YOLOv11 to be the most appropriate model,providing an ideal balance between accuracy,computational efficiency,and robustness for real-world deployment.Nevertheless,there exist certain issues to be addressed,particularly in discriminating against visually similar activities and the use of publicly available datasets.Future research will entail the inclusion of 3D data and multimodal sensor inputs,such as depth and motion information,for enhancing recognition accuracy and generalizability to challenging real-world environments.
基金Supported by the Research Chair of Online Dialogue and Cultural Communication,King Saud University,Saudi Arabia.
文摘Automated recognition of violent activities from videos is vital for public safety,but often raises significant privacy concerns due to the sensitive nature of the footage.Moreover,resource constraints often hinder the deployment of deep learning-based complex video classification models on edge devices.With this motivation,this study aims to investigate an effective violent activity classifier while minimizing computational complexity,attaining competitive performance,and mitigating user data privacy concerns.We present a lightweight deep learning architecture with fewer parameters for efficient violent activity recognition.We utilize a two-stream formation of 3D depthwise separable convolution coupled with a linear self-attention mechanism for effective feature extraction,incorporating federated learning to address data privacy concerns.Experimental findings demonstrate the model’s effectiveness with test accuracies from 96%to above 97%on multiple datasets by incorporating the FedProx aggregation strategy.These findings underscore the potential to develop secure,efficient,and reliable solutions for violent activity recognition in real-world scenarios.
基金funded by the Ongoing Research Funding Program(ORF-2025-890),King Saud University,Riyadh,Saudi Arabia.
文摘Activity recognition is a challenging topic in the field of computer vision that has various applications,including surveillance systems,industrial automation,and human-computer interaction.Today,the demand for automation has greatly increased across industries worldwide.Real-time detection requires edge devices with limited computational time.This study proposes a novel hybrid deep learning system for human activity recognition(HAR),aiming to enhance the recognition accuracy and reduce the computational time.The proposed system combines a pretrained image classification model with a sequence analysis model.First,the dataset was divided into a training set(70%),validation set(10%),and test set(20%).Second,all the videos were converted into frames and deep-based features were extracted from each frame using convolutional neural networks(CNNs)with a vision transformer.Following that,bidirectional long short-term memory(BiLSTM)-and temporal convolutional network(TCN)-based models were trained using the training set,and their performances were evaluated using the validation set and test set.Four benchmark datasets(UCF11,UCF50,UCF101,and JHMDB)were used to evaluate the performance of the proposed HAR-based system.The experimental results showed that the combination of ConvNeXt and the TCN-based model achieved a recognition accuracy of 97.73%for UCF11,98.81%for UCF50,98.46%for UCF101,and 83.38%for JHMDB,respectively.This represents improvements in the recognition accuracy of 4%,2.67%,3.67%,and 7.08%for the UCF11,UCF50,UCF101,and JHMDB datasets,respectively,over existing models.Moreover,the proposed HAR-based system obtained superior recognition accuracy,shorter computational times,and minimal memory usage compared to the existing models.
基金supported by the BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Education(NRF5199991014091)Seok-Won Lee’s work was supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)under the Artificial Intelligence Convergence Innovation Human Resources Development(IITP-2024-RS-2023-00255968)grant funded by the Korea government(MSIT).
文摘This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities,and proposes an optimized ontology framework to improve recognition accuracy and computational efficiency.The method in this paper adopts the event sequence segmentation technique,combines location awareness with time interval reasoning,and improves human activity recognition through ontology reasoning.Compared with the existing methods,the framework performs better when dealing with uncertain data and complex scenes,and the experimental results show that its recognition accuracy is improved by 15.6%and processing time is reduced by 22.4%.In addition,it is found that with the increase of context complexity,the traditional ontology inferencemodel has limitations in abnormal behavior recognition,especially in the case of high data redundancy,which tends to lead to a decrease in recognition accuracy.This study effectively mitigates this problem by optimizing the ontology matching algorithm and combining parallel computing and deep learning techniques to enhance the activity recognition capability in complex environments.
基金supported by the Royal Golden Jubilee(RGJ)Ph.D.Programme(Grant No.PHD/0079/2561)through the National Research Council of Thailand(NRCT)and Thailand Research Fund(TRF).
文摘This research investigates the application of multisource data fusion using a Multi-Layer Perceptron (MLP) for Human Activity Recognition (HAR). The study integrates four distinct open-source datasets—WISDM, DaLiAc, MotionSense, and PAMAP2—to develop a generalized MLP model for classifying six human activities. Performance analysis of the fused model for each dataset reveals accuracy rates of 95.83 for WISDM, 97 for DaLiAc, 94.65 for MotionSense, and 98.54 for PAMAP2. A comparative evaluation was conducted between the fused MLP model and the individual dataset models, with the latter tested on separate validation sets. The results indicate that the MLP model, trained on the fused dataset, exhibits superior performance relative to the models trained on individual datasets. This finding suggests that multisource data fusion significantly enhances the generalization and accuracy of HAR systems. The improved performance underscores the potential of integrating diverse data sources to create more robust and comprehensive models for activity recognition.
文摘Human activity recognition is commonly used in several Internet of Things applications to recognize different contexts and respond to them.Deep learning has gained momentum for identifying activities through sensors,smartphones or even surveillance cameras.However,it is often difficult to train deep learning models on constrained IoT devices.The focus of this paper is to propose an alternative model by constructing a Deep Learning-based Human Activity Recognition framework for edge computing,which we call DL-HAR.The goal of this framework is to exploit the capabilities of cloud computing to train a deep learning model and deploy it on less-powerful edge devices for recognition.The idea is to conduct the training of the model in the Cloud and distribute it to the edge nodes.We demonstrate how the DL-HAR can perform human activity recognition at the edge while improving efficiency and accuracy.In order to evaluate the proposed framework,we conducted a comprehensive set of experiments to validate the applicability of DL-HAR.Experimental results on the benchmark dataset show a significant increase in performance compared with the state-of-the-art models.
基金supported by National Natural Science Foundation of China(Nos.61976010,61802011)Beijing Postdoctoral Research Foundation(No.ZZ2019-63)+1 种基金Beijing excellent young talent cultivation project(No.2017000020124G075)“Ri xin”Training Programme Foundation for the Talents by Beijing University of Technology。
文摘Human group activity recognition(GAR)has attracted significant attention from computer vision researchers due to its wide practical applications in security surveillance,social role understanding and sports video analysis.In this paper,we give a comprehensive overview of the advances in group activity recognition in videos during the past 20 years.First,we provide a summary and comparison of 11 GAR video datasets in this field.Second,we survey the group activity recognition methods,including those based on handcrafted features and those based on deep learning networks.For better understanding of the pros and cons of these methods,we compare various models from the past to the present.Finally,we outline several challenging issues and possible directions for future research.From this comprehensive literature review,readers can obtain an overview of progress in group activity recognition for future studies.
基金supported in part by the National Natural Science Foundation of China under Grants(62250410365,62071084)the Guangdong Basic and Applied Basic Research Foundation of China(2022A1515011542)the Guangzhou Science and technology program of China(202201010606).
文摘With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation.However,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing data.It is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational complexity.Visual Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images.The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT.In this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved.By diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is mitigated.By fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing images.The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.
文摘Human activity tracking plays a vital role in human–computer interaction.Traditional human activity recognition(HAR)methods adopt special devices,such as cameras and sensors,to track both macro-and micro-activities.Recently,wireless signals have been exploited to track human motion and activities in indoor environments without additional equipment.This study proposes a device-free WiFi-based micro-activity recognition method that leverages the channel state information(CSI)of wireless signals.Different from existed CSI-based microactivity recognition methods,the proposed method extracts both amplitude and phase information from CSI,thereby providing more information and increasing detection accuracy.The proposed method harnesses an effective signal processing technique to reveal the unique patterns of each activity.We applied a machine learning algorithm to recognize the proposed micro-activities.The proposed method has been evaluated in both line of sight(LOS)and none line of sight(NLOS)scenarios,and the empirical results demonstrate the effectiveness of the proposed method with several users.
基金supported by the National Natural Science Foundation of China(60573159)the Guangdong High Technique Project(201100000514)
文摘This paper proposes a hybrid approach for recognizing human activities from trajectories. First, an improved hidden Markov model （HMM） parameter learning algorithm, HMM-PSO, is proposed, which achieves a better balance between the global and local exploitation by the nonlinear update strategy and repulsion operation. Then, the event probability sequence （EPS） which consists of a series of events is computed to describe the unique characteristic of human activities. The anatysis on EPS indicates that it is robust to the changes in viewing direction and contributes to improving the recognition rate. Finally, the effectiveness of the proposed approach is evaluated by data experiments on current popular datasets.
基金This work was supported by financial support from Universiti Sains Malaysia(USM)under FRGS grant number FRGS/1/2020/TK03/USM/02/1the School of Computer Sciences USM for their support.
文摘Human Activity Recognition(HAR)is an active research area due to its applications in pervasive computing,human-computer interaction,artificial intelligence,health care,and social sciences.Moreover,dynamic environments and anthropometric differences between individuals make it harder to recognize actions.This study focused on human activity in video sequences acquired with an RGB camera because of its vast range of real-world applications.It uses two-stream ConvNet to extract spatial and temporal information and proposes a fine-tuned deep neural network.Moreover,the transfer learning paradigm is adopted to extract varied and fixed frames while reusing object identification information.Six state-of-the-art pre-trained models are exploited to find the best model for spatial feature extraction.For temporal sequence,this study uses dense optical flow following the two-stream ConvNet and Bidirectional Long Short TermMemory(BiLSTM)to capture longtermdependencies.Two state-of-the-art datasets,UCF101 and HMDB51,are used for evaluation purposes.In addition,seven state-of-the-art optimizers are used to fine-tune the proposed network parameters.Furthermore,this study utilizes an ensemble mechanism to aggregate spatial-temporal features using a four-stream Convolutional Neural Network(CNN),where two streams use RGB data.In contrast,the other uses optical flow images.Finally,the proposed ensemble approach using max hard voting outperforms state-ofthe-art methods with 96.30%and 90.07%accuracies on the UCF101 and HMDB51 datasets.
基金supported by a grant (2021R1F1A1063634)of the Basic Science Research Program through the National Research Foundation (NRF)funded by the Ministry of Education,Republic of Korea.
文摘Hand gesture recognition (HGR) is used in a numerous applications,including medical health-care, industrial purpose and sports detection.We have developed a real-time hand gesture recognition system using inertialsensors for the smart home application. Developing such a model facilitatesthe medical health field (elders or disabled ones). Home automation has alsobeen proven to be a tremendous benefit for the elderly and disabled. Residentsare admitted to smart homes for comfort, luxury, improved quality of life,and protection against intrusion and burglars. This paper proposes a novelsystem that uses principal component analysis, linear discrimination analysisfeature extraction, and random forest as a classifier to improveHGRaccuracy.We have achieved an accuracy of 94% over the publicly benchmarked HGRdataset. The proposed system can be used to detect hand gestures in thehealthcare industry as well as in the industrial and educational sectors.
基金Project(50808025) supported by the National Natural Science Foundation of ChinaProject(20090162110057) supported by the Doctoral Fund of Ministry of Education,China
文摘A new method for complex activity recognition in videos by key frames was presented. The progressive bisection strategy(PBS) was employed to divide the complex activity into a series of simple activities and the key frames representing the simple activities were extracted by the self-splitting competitive learning(SSCL) algorithm. A new similarity criterion of complex activities was defined. Besides the regular visual factor, the order factor and the interference factor measuring the timing matching relationship of the simple activities and the discontinuous matching relationship of the simple activities respectively were considered. On these bases, the complex human activity recognition could be achieved by calculating their similarities. The recognition error was reduced compared with other methods when ignoring the recognition of simple activities. The proposed method was tested and evaluated on the self-built broadcast gymnastic database and the dancing database. The experimental results prove the superior efficiency.
基金supported by the National Natural Science Foundation of China under Grants No.61075045,No.61273256the Program for New Century Excellent Talents in University under Grant No.NECT-10-0292+1 种基金the National Key Basic Research Program of China(973Program)under Grant No.2011-CB707000the Fundamental Research Funds for the Central Universities
文摘We study the problem of humanactivity recognition from RGB-Depth(RGBD)sensors when the skeletons are not available.The skeleton tracking in Kinect SDK workswell when the human subject is facing thecamera and there are no occlusions.In surveillance or nursing home monitoring scenarios,however,the camera is usually mounted higher than human subjects,and there may beocclusions.The interest-point based approachis widely used in RGB based activity recognition,it can be used in both RGB and depthchannels.Whether we should extract interestpoints independently of each channel or extract interest points from only one of thechannels is discussed in this paper.The goal ofthis paper is to compare the performances ofdifferent methods of extracting interest points.In addition,we have developed a depth mapbased descriptor and built an RGBD dataset,called RGBD-SAR,for senior activity recognition.We show that the best performance isachieved when we extract interest points solely from RGB channels,and combine the RGBbased descriptors with the depth map-baseddescriptors.We also present a baseline performance of the RGBD-SAR dataset.
基金supported by Korea Institute for Advancement of Technology(KIAT)grant fundedthe Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)the Soonchunhyang University Research Fund.
文摘Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and entertainment.Since wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor values.Further,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,etc.Recently-developed Artificial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR process.With this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare environment.The proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare service.In addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the out-liers.IHPTDL-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of users.Moreover,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN model.Finally,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different aspects.The experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures.
基金This work was supported by Foshan Science and Technology Innovation Special Fund Project(No.BK22BF004 and No.BK20AF004),Guangdong Province,China.
文摘Activity and motion recognition using Wi-Fi signals,mainly channel state information(CSI),has captured the interest of many researchers in recent years.Many research studies have achieved splendid results with the help of machine learning models from different applications such as healthcare services,sign language translation,security,context awareness,and the internet of things.Nevertheless,most of these adopted studies have some shortcomings in the machine learning algorithms as they rely on recurrence and convolutions and,thus,precluding smooth sequential computation.Therefore,in this paper,we propose a deep-learning approach based solely on attention,i.e.,the sole Self-Attention Mechanism model(Sole-SAM),for activity and motion recognition using Wi-Fi signals.The Sole-SAM was deployed to learn the features representing different activities and motions from the raw CSI data.Experiments were carried out to evaluate the performance of the proposed Sole-SAM architecture.The experimental results indicated that our proposed system took significantly less time to train than models that rely on recurrence and convolutions like Long Short-Term Memory(LSTM)and Recurrent Neural Network(RNN).Sole-SAM archived a 0.94%accuracy level,which is 0.04%better than RNN and 0.02%better than LSTM.
基金supported by the Guangxi University of Science and Technology,Liuzhou,China,sponsored by the Researchers Supporting Project(No.XiaoKeBo21Z27,The Construction of Electronic Information Team supported by Artificial Intelligence Theory and Three-dimensional Visual Technology,Yuesheng Zhao)supported by the 2022 Laboratory Fund Project of the Key Laboratory of Space-Based Integrated Information System(No.SpaceInfoNet20221120,Research on the Key Technologies of Intelligent Spatiotemporal Data Engine Based on Space-Based Information Network,Yuesheng Zhao)supported by the 2023 Guangxi University Young and Middle-Aged Teachers’Basic Scientific Research Ability Improvement Project(No.2023KY0352,Research on the Recognition of Psychological Abnormalities in College Students Based on the Fusion of Pulse and EEG Techniques,Yutong Luo).
文摘With the rapid advancement of wearable devices,Human Activities Recognition(HAR)based on these devices has emerged as a prominent research field.The objective of this study is to enhance the recognition performance of HAR by proposing an LSTM-1DCNN recognition algorithm that utilizes a single triaxial accelerometer.This algorithm comprises two branches:one branch consists of a Long and Short-Term Memory Network(LSTM),while the other parallel branch incorporates a one-dimensional Convolutional Neural Network(1DCNN).The parallel architecture of LSTM-1DCNN initially extracts spatial and temporal features from the accelerometer data separately,which are then concatenated and fed into a fully connected neural network for information fusion.In the LSTM-1DCNN architecture,the 1DCNN branch primarily focuses on extracting spatial features during convolution operations,whereas the LSTM branch mainly captures temporal features.Nine sets of accelerometer data from five publicly available HAR datasets are employed for training and evaluation purposes.The performance of the proposed LSTM-1DCNN model is compared with five other HAR algorithms including Decision Tree,Random Forest,Support Vector Machine,1DCNN,and LSTM on these five public datasets.Experimental results demonstrate that the F1-score achieved by the proposed LSTM-1DCNN ranges from 90.36%to 99.68%,with a mean value of 96.22%and standard deviation of 0.03 across all evaluated metrics on these five public datasets-outperforming other existing HAR algorithms significantly in terms of evaluation metrics used in this study.Finally the proposed LSTM-1DCNN is validated in real-world applications by collecting acceleration data of seven human activities for training and testing purposes.Subsequently,the trained HAR algorithm is deployed on Android phones to evaluate its performance.Experimental results demonstrate that the proposed LSTM-1DCNN algorithm achieves an impressive F1-score of 97.67%on our self-built dataset.In conclusion,the fusion of temporal and spatial information in the measured data contributes to the excellent HAR performance and robustness exhibited by the proposed 1DCNN-LSTM architecture.
基金funded by the National Science and Technology Council,Taiwan(Grant No.NSTC 112-2121-M-039-001)by China Medical University(Grant No.CMU112-MF-79).
文摘Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.
基金supported by the Guangxi University of Science and Technology,Liuzhou,China,sponsored by the Researchers Supporting Project(No.XiaoKeBo21Z27,The Construction of Electronic Information Team Supported by Artificial Intelligence Theory and ThreeDimensional Visual Technology,Yuesheng Zhao)supported by the Key Laboratory for Space-based Integrated Information Systems 2022 Laboratory Funding Program(No.SpaceInfoNet20221120,Research on the Key Technologies of Intelligent Spatio-Temporal Data Engine Based on Space-Based Information Network,Yuesheng Zhao)supported by the 2023 Guangxi University Young and Middle-Aged Teachers’Basic Scientific Research Ability Improvement Project(No.2023KY0352,Research on the Recognition of Psychological Abnormalities in College Students Based on the Fusion of Pulse and EEG Techniques,Yutong Lu).
文摘The purpose of Human Activities Recognition(HAR)is to recognize human activities with sensors like accelerometers and gyroscopes.The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms.In this paper,we experimentally validate the HAR process and its various algorithms independently.On the base of which,it is further proposed that,in addition to the necessary eigenvalues and intelligent algorithms,correct prior knowledge is even more critical.The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object,the sampling process,the sampling data,the HAR algorithm,etc.Thus,a solution is presented under the guidance of right prior knowledge,using Back-Propagation neural networks(BP networks)and simple Convolutional Neural Networks(CNN).The results show that HAR can be achieved with 90%–100%accuracy.Further analysis shows that intelligent algorithms for pattern recognition and classification problems,typically represented by HAR,require correct prior knowledge to work effectively.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R194)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.
