Human activities in a transborder watershed are complex under the influence of domestic policies,international relations,and global events.Understanding the forces driving human activity change is important for the de...Human activities in a transborder watershed are complex under the influence of domestic policies,international relations,and global events.Understanding the forces driving human activity change is important for the development of transborder watershed.In this study,we used global historical land cover data,the hemeroby index model,and synthesized major historical events to analyze how human activity intensity changed in the Heilongjiang River(Amur River in Russia)watershed(HLRW).The results showed that there was a strong spatial heterogeneity in the variation of human activity intensity in the HLRW over the past century(1900-2016).On the Chinese side,the human activity intensity change shifted from the plain areas for agricultural reclamation to the mountainous areas for timber extraction.On the Russian side,human activity intensity changes mostly concentrated along the Trans-Siberian Railway and the Baikal-Amur Mainline.Localized variation of human activity intensity tended to respond to regional events while regionalized variation tends to reflect national policy change or broad international events.The similarities and differences between China and Russia in policies and positions in international events resulted in synchronous and asynchronous changes in human activity intensity.Meanwhile,policy shifts were often confined by the natural features of the watershed.These results reveal the historical origins and fundamental connotations of watershed development and contribute to formulating regional management policies that coordinate population,eco-nomic,social,and environmental activities.展开更多
RFID-based human activity recognition(HAR)attracts attention due to its convenience,noninvasiveness,and privacy protection.Existing RFID-based HAR methods use modeling,CNN,or LSTM to extract features effectively.Still...RFID-based human activity recognition(HAR)attracts attention due to its convenience,noninvasiveness,and privacy protection.Existing RFID-based HAR methods use modeling,CNN,or LSTM to extract features effectively.Still,they have shortcomings:1)requiring complex hand-crafted data cleaning processes and 2)only addressing single-person activity recognition based on specific RF signals.To solve these problems,this paper proposes a novel device-free method based on Time-streaming Multiscale Transformer called TransTM.This model leverages the Transformer's powerful data fitting capabilities to take raw RFID RSSI data as input without pre-processing.Concretely,we propose a multiscale convolutional hybrid Transformer to capture behavioral features that recognizes singlehuman activities and human-to-human interactions.Compared with existing CNN-and LSTM-based methods,the Transformer-based method has more data fitting power,generalization,and scalability.Furthermore,using RF signals,our method achieves an excellent classification effect on human behaviorbased classification tasks.Experimental results on the actual RFID datasets show that this model achieves a high average recognition accuracy(99.1%).The dataset we collected for detecting RFID-based indoor human activities will be published.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
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
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
Traditional indoor human activity recognition(HAR)is a timeseries data classification problem and needs feature extraction.Presently,considerable attention has been given to the domain ofHARdue to the enormous amount ...Traditional indoor human activity recognition(HAR)is a timeseries data classification problem and needs feature extraction.Presently,considerable attention has been given to the domain ofHARdue to the enormous amount of its real-time uses in real-time applications,namely surveillance by authorities,biometric user identification,and health monitoring of older people.The extensive usage of the Internet of Things(IoT)and wearable sensor devices has made the topic of HAR a vital subject in ubiquitous and mobile computing.The more commonly utilized inference and problemsolving technique in the HAR system have recently been deep learning(DL).The study develops aModifiedWild Horse Optimization withDLAided Symmetric Human Activity Recognition(MWHODL-SHAR)model.The major intention of the MWHODL-SHAR model lies in recognition of symmetric activities,namely jogging,walking,standing,sitting,etc.In the presented MWHODL-SHAR technique,the human activities data is pre-processed in various stages to make it compatible for further processing.A convolution neural network with an attention-based long short-term memory(CNNALSTM)model is applied for activity recognition.The MWHO algorithm is utilized as a hyperparameter tuning strategy to improve the detection rate of the CNN-ALSTM algorithm.The experimental validation of the MWHODL-SHAR technique is simulated using a benchmark dataset.An extensive comparison study revealed the betterment of theMWHODL-SHAR technique over other recent approaches.展开更多
The influences of human activity on regional climate over China have been widely reported and drawn great attention from both the scientific community and governments.This paper reviews the evidence of the anthropogen...The influences of human activity on regional climate over China have been widely reported and drawn great attention from both the scientific community and governments.This paper reviews the evidence of the anthropogenic influence on regional climate over China from the perspectives of surface air temperature(SAT),precipitation,droughts,and surface wind speed,based on studies published since 2018.The reviewed evidence indicates that human activities,including greenhouse gas and anthropogenic aerosol emissions,land use and cover change,urbanization,and anthropogenic heat release,have contributed to changes in the SAT trend and the likelihood of regional record-breaking extreme high/low temperature events over China.The anthropogenically forced SAT signal can be detected back to the 1870s in the southeastern Tibetan Plateau region.Although the anthropogenic signal of summer precipitation over China is detectable and anthropogenic forcing has contributed to an increased likelihood of regional record-breaking heavy/low precipitation events,the anthropogenic precipitation signal over China is relatively obscure.Moreover,human activities have also contributed to a decline in surface wind speed,weakening of monsoon precipitation,and an increase in the frequency of droughts and compound extreme climate/weather events over China in recent decades.This review can serve as a reference both for further understanding the causes of regional climate changes over China and for sound decision-making on regional climate mitigation and adaptation.Additionally,a few key or challenging scientific issues associated with the human influence on regional climate changes are discussed in the context of future research.展开更多
Investigating the spatiotemporal variation of human activity intensity and its determinants is a crucial basis for further revealing the mechanism of human-environment interaction and optimizing the human development ...Investigating the spatiotemporal variation of human activity intensity and its determinants is a crucial basis for further revealing the mechanism of human-environment interaction and optimizing the human development mode.In this study,the human activity intensity on the Qinghai-Tibet Plateau(QTP)from 1990 to 2020 was measured based on the quantitative model of land use data and the actual regional background,and the under-lying natural and socioeconomic determinants were investigated using spatial econometric methods.The results demonstrate that(1)the human activity intensity in QTP has increased by 11.96%,and there are differences in different spatial scales;the areas with high human activity intensity are distributed in the Hehuang Valley where Xining City and its surrounding areas are located,as well as the One-River and Two-River Area where Lhasa City and surrounding areas are located.(2)Human activity intensity has significant positive spatial spillover,suggesting that local changes will cause changes in the same direction in adjacent areas.(3)The human activ-ity intensity in QTP is affected by various determinants.Concerning socioeconomic factors,the economic level has no significant impact on the human activity intensity in QTP,which differs from the general regional law.Both urbanization and traffic conditions have a significant positive effect,and the impact intensity continues to increase.Concerning natural factors,topographic relief has a significant positive effect;the impacts of temper-ature and vegetation coverage have changed from insignificant to a significant positive effect;the impacts of precipitation and river network density have not been verified;there is no linear relationship between altitude and human activity intensity in the entire QTP,while it exists in local regions.Finally,this study proposes three policy implications for the realization of a more harmonious human-environment relationship in QTP.展开更多
Human Action Recognition(HAR)and pose estimation from videos have gained significant attention among research communities due to its applica-tion in several areas namely intelligent surveillance,human robot interaction...Human Action Recognition(HAR)and pose estimation from videos have gained significant attention among research communities due to its applica-tion in several areas namely intelligent surveillance,human robot interaction,robot vision,etc.Though considerable improvements have been made in recent days,design of an effective and accurate action recognition model is yet a difficult process owing to the existence of different obstacles such as variations in camera angle,occlusion,background,movement speed,and so on.From the literature,it is observed that hard to deal with the temporal dimension in the action recognition process.Convolutional neural network(CNN)models could be used widely to solve this.With this motivation,this study designs a novel key point extraction with deep convolutional neural networks based pose estimation(KPE-DCNN)model for activity recognition.The KPE-DCNN technique initially converts the input video into a sequence of frames followed by a three stage process namely key point extraction,hyperparameter tuning,and pose estimation.In the keypoint extraction process an OpenPose model is designed to compute the accurate key-points in the human pose.Then,an optimal DCNN model is developed to classify the human activities label based on the extracted key points.For improving the training process of the DCNN technique,RMSProp optimizer is used to optimally adjust the hyperparameters such as learning rate,batch size,and epoch count.The experimental results tested using benchmark dataset like UCF sports dataset showed that KPE-DCNN technique is able to achieve good results compared with benchmark algorithms like CNN,DBN,SVM,STAL,T-CNN and so on.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
Human-Computer Interaction(HCI)is a sub-area within computer science focused on the study of the communication between people(users)and computers and the evaluation,implementation,and design of user interfaces for com...Human-Computer Interaction(HCI)is a sub-area within computer science focused on the study of the communication between people(users)and computers and the evaluation,implementation,and design of user interfaces for computer systems.HCI has accomplished effective incorporation of the human factors and software engineering of computing systems through the methods and concepts of cognitive science.Usability is an aspect of HCI dedicated to guar-anteeing that human–computer communication is,amongst other things,efficient,effective,and sustaining for the user.Simultaneously,Human activity recognition(HAR)aim is to identify actions from a sequence of observations on the activities of subjects and the environmental conditions.The vision-based HAR study is the basis of several applications involving health care,HCI,and video surveillance.This article develops a Fire Hawk Optimizer with Deep Learning Enabled Activ-ity Recognition(FHODL-AR)on HCI driven usability.In the presented FHODL-AR technique,the input images are investigated for the identification of different human activities.For feature extraction,a modified SqueezeNet model is intro-duced by the inclusion of few bypass connections to the SqueezeNet among Fire modules.Besides,the FHO algorithm is utilized as a hyperparameter optimization algorithm,which in turn boosts the classification performance.To detect and cate-gorize different kinds of activities,probabilistic neural network(PNN)classifier is applied.The experimental validation of the FHODL-AR technique is tested using benchmark datasets,and the outcomes reported the improvements of the FHODL-AR technique over other recent approaches.展开更多
Human Activity Recognition(HAR)from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications,video-based behavior monitoring,video classi...Human Activity Recognition(HAR)from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications,video-based behavior monitoring,video classification,and indexing,patient monitoring,robotics,and behavior analysis.Although many techniques are available for HAR in video analysis tasks,most of them are not focusing on behavioral analysis.Hence,a new HAR system analysis the behavioral activity of a person based on the deep learning approach proposed in this work.The most essential aim of this work is to recognize the complex activities that are useful in many tasks that are based on object detection,modelling of individual frame characteristics,and communication among them.Moreover,this work focuses on finding out the human actions from various video resolutions,invariant human poses,and nearness of multi objects.First,we identify the key and essential frames of each activity using histogram differences.Secondly,Discrete Wavelet Transform(DWT)is used in this system to extract coefficients from the sequence of key-frames where the activity is localized in space.Finally,an Adaptive Weighted Flow Net(AWFN)algorithm is proposed in this work for effective video activity recognition.Moreover,the proposed algorithm has been evaluated by comparing it with the existing Visual Geometry Group(VGG-16)convolution neural networks for making performance comparisons.This work focuses on competent deep learning-based feature extraction to discriminate the activities for performing the classification accuracy.The proposed model has been evaluated with VGG-16 using a combination of regular UCF-101 activity datasets and also in very challenging Low-quality videos such as HMDB51.From these investigations,it is proved that the proposed AWFN approach gives higher detection accuracy of 96%.It is approximately 0.3%to 7.88%of higher accuracy than state-of-art methods.展开更多
With the development of wireless technology, Frequency-Modulated Continuous Wave (FMCW) radar has increased sensing capability and can be used to recognize human activity. These applications have gained wide-spread at...With the development of wireless technology, Frequency-Modulated Continuous Wave (FMCW) radar has increased sensing capability and can be used to recognize human activity. These applications have gained wide-spread attention and become a hot research area. FMCW signals reflected by target activity can be collected, and human activity can be recognized based on the measurements. This paper focused on human activity recognition based on FMCW and DenseNet. We collected point clouds from FMCW and analyzed them to recognize human activity because different activities could lead to unique point cloud features. We built and trained the neural network to implement human activities using a FMCW signal. Firstly, this paper presented recent reviews about human activity recognition using wireless signals. Then, it introduced the basic concepts of FMCW radar and described the fundamental principles of the system using FMCW radar. We also provided the system framework, experiment scenario, and DenseNet neural network structure. Finally, we presented the experimental results and analyzed the accuracy of different neural network models. The system achieved recognition accuracy of 100 percent for five activities using the DenseNet. We concluded the paper by discussing the current issues and future research directions.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
The applicability of the laws of thermodynamics to processes on Earth is discussed and it is shown that the chemical thermodynamics provides the reasonable basis for predicting probable changes. The historical evoluti...The applicability of the laws of thermodynamics to processes on Earth is discussed and it is shown that the chemical thermodynamics provides the reasonable basis for predicting probable changes. The historical evolution of the planet is considered in the framework of the Harari approach;a civilization’s level is estimated by the Kardashev scale based on the amount of energy it is able to use. During a short historical interval (≈500 years), when the effect of biological evolution is imperceptible and the main changes on the planet are caused by human activity, two systems are considered: 1) a nonequilibrium inhabited planet and 2) a quasi-equilibrium hypothetical planet without people, which is accepted as a comparison system. It is shown that in response to the energy impact, the equilibrium of the hypothetical system with the primordial nature is disturbed and processes are initiated aimed to prevent further growth of energy production. In the case of a real planet, this implements changes preventing the uncontrolled activities of humans—the energy producers. Climate change, an increase in the number of natural disasters and epidemics can be recognized as a direct response of the planet, while changes in socio-economic relations, morality, demographic situation, new threats etc. can be considered as an indirect reaction to changing conditions of human beings. The latter results from the mutual correlation between the progress of society, on the one hand, and humanitarian and political processes, on the other. The role of renewable and non-renewable energy sources in evolution is taken into account. Obviously, it is better to take meaningful measures to achieve an acceptable balance now than to wait for the blind and extremely painful action of the laws of nature, which would lead to a reduction in the population.展开更多
With the improvement of people’s living standards,the demand for health monitoring and exercise detection is increasing.It is of great significance to study human activity recognition(HAR)methods that are different f...With the improvement of people’s living standards,the demand for health monitoring and exercise detection is increasing.It is of great significance to study human activity recognition(HAR)methods that are different from traditional feature extraction methods.This article uses convolutional neural network(CNN)algorithms in deep learning to automatically extract features of activities related to human life.We used a stochastic gradient descent algorithm to optimize the parameters of the CNN.The trained network model is compressed on STM32CubeMX-AI.Finally,this article introduces the use of neural networks on embedded devices to recognize six human activities of daily life,such as sitting,standing,walking,jogging,upstairs,and downstairs.The acceleration sensor related to human activity information is used to obtain the relevant characteristics of the activity,thereby solving the HAR problem.By drawing the accuracy curve,loss function curve,and confusion matrix diagram of the training model,the recognition effect of the convolutional neural network can be seen more intuitively.After comparing the average accuracy of each set of experiments and the test set of the best model obtained from it,the best model is then selected.展开更多
Nowadays,the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data.The video datasets are generated using cameras avai...Nowadays,the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data.The video datasets are generated using cameras available in various devices that can be in a static or dynamic position and are referred to as untrimmed videos.Smarter monitoring is a historical necessity in which commonly occurring,regular,and out-of-the-ordinary activities can be automatically identified using intelligence systems and computer vision technology.In a long video,human activity may be present anywhere in the video.There can be a single ormultiple human activities present in such videos.This paper presents a deep learning-based methodology to identify the locally present human activities in the video sequences captured by a single wide-view camera in a sports environment.The recognition process is split into four parts:firstly,the video is divided into different set of frames,then the human body part in a sequence of frames is identified,next process is to identify the human activity using a convolutional neural network and finally the time information of the observed postures for each activity is determined with the help of a deep learning algorithm.The proposed approach has been tested on two different sports datasets including ActivityNet and THUMOS.Three sports activities like swimming,cricket bowling and high jump have been considered in this paper and classified with the temporal information i.e.,the start and end time for every activity present in the video.The convolutional neural network and long short-term memory are used for feature extraction of temporal action recognition from video data of sports activity.The outcomes show that the proposed method for activity recognition in the sports domain outperforms the existing methods.展开更多
With the emerging of sensor networks, research on sensor-based activity recognition has attracted much attention. Many existing methods cannot well deal with the cases that contain hundreds of sensors and their recogn...With the emerging of sensor networks, research on sensor-based activity recognition has attracted much attention. Many existing methods cannot well deal with the cases that contain hundreds of sensors and their recognition accuracy is requisite to be further improved. A novel framework for recognizing human activities in smart home was presented. First, small, easy-to-install, and low-cost state change sensors were adopted for recording state change or use of the objects. Then the Bayesian belief network (BBN) was applied to conducting activity recognition by modeling statistical dependencies between sensor data and human activity. An edge-encode genetic algorithm (EEGA) approach was proposed to resolve the difficulties in structure learning of the BBN model under a high dimension space and large data set. Finally, some experiments were made using one publicly available dataset. The experimental results show that the EEGA algorithm is effective and efficient in learning the BBN structure and outperforms the conventional approaches. By conducting human activity recognition based on the testing samples, the BBN is effective to conduct human activity recognition and outperforms the naive Bayesian network (NBN) and multiclass naive Bayes classifier (MNBC).展开更多
Device-free activity recognition plays a crucial role in smart building,security,and human–computer interaction,which shows its strength in its convenience and cost-efficiency.Traditional machine learning has made si...Device-free activity recognition plays a crucial role in smart building,security,and human–computer interaction,which shows its strength in its convenience and cost-efficiency.Traditional machine learning has made significant progress by heuristic hand-crafted features and statistical models,but it suffers from the limitation of manual feature design.Deep learning overcomes such issues by automatic high-level feature extraction,but its performance degrades due to the requirement of massive annotated data and cross-site issues.To deal with these problems,transfer learning helps to transfer knowledge from existing datasets while dealing with the negative effect of background dynamics.This paper surveys the recent progress of deep learning and transfer learning for device-free activity recognition.We begin with the motivation of deep learning and transfer learning,and then introduce the major sensor modalities.Then the deep and transfer learning techniques for device-free human activity recognition are introduced.Eventually,insights on existing works and grand challenges are summarized and presented to promote future research.展开更多
基金Under the auspices of National Key Research and Development Program of China(No.2017YFA0604403)National Natural Science Foundation of China(No.41801108)。
文摘Human activities in a transborder watershed are complex under the influence of domestic policies,international relations,and global events.Understanding the forces driving human activity change is important for the development of transborder watershed.In this study,we used global historical land cover data,the hemeroby index model,and synthesized major historical events to analyze how human activity intensity changed in the Heilongjiang River(Amur River in Russia)watershed(HLRW).The results showed that there was a strong spatial heterogeneity in the variation of human activity intensity in the HLRW over the past century(1900-2016).On the Chinese side,the human activity intensity change shifted from the plain areas for agricultural reclamation to the mountainous areas for timber extraction.On the Russian side,human activity intensity changes mostly concentrated along the Trans-Siberian Railway and the Baikal-Amur Mainline.Localized variation of human activity intensity tended to respond to regional events while regionalized variation tends to reflect national policy change or broad international events.The similarities and differences between China and Russia in policies and positions in international events resulted in synchronous and asynchronous changes in human activity intensity.Meanwhile,policy shifts were often confined by the natural features of the watershed.These results reveal the historical origins and fundamental connotations of watershed development and contribute to formulating regional management policies that coordinate population,eco-nomic,social,and environmental activities.
基金the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDC02040300)for this study.
文摘RFID-based human activity recognition(HAR)attracts attention due to its convenience,noninvasiveness,and privacy protection.Existing RFID-based HAR methods use modeling,CNN,or LSTM to extract features effectively.Still,they have shortcomings:1)requiring complex hand-crafted data cleaning processes and 2)only addressing single-person activity recognition based on specific RF signals.To solve these problems,this paper proposes a novel device-free method based on Time-streaming Multiscale Transformer called TransTM.This model leverages the Transformer's powerful data fitting capabilities to take raw RFID RSSI data as input without pre-processing.Concretely,we propose a multiscale convolutional hybrid Transformer to capture behavioral features that recognizes singlehuman activities and human-to-human interactions.Compared with existing CNN-and LSTM-based methods,the Transformer-based method has more data fitting power,generalization,and scalability.Furthermore,using RF signals,our method achieves an excellent classification effect on human behaviorbased classification tasks.Experimental results on the actual RFID datasets show that this model achieves a high average recognition accuracy(99.1%).The dataset we collected for detecting RFID-based indoor human activities will be published.
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.
文摘Traditional indoor human activity recognition(HAR)is a timeseries data classification problem and needs feature extraction.Presently,considerable attention has been given to the domain ofHARdue to the enormous amount of its real-time uses in real-time applications,namely surveillance by authorities,biometric user identification,and health monitoring of older people.The extensive usage of the Internet of Things(IoT)and wearable sensor devices has made the topic of HAR a vital subject in ubiquitous and mobile computing.The more commonly utilized inference and problemsolving technique in the HAR system have recently been deep learning(DL).The study develops aModifiedWild Horse Optimization withDLAided Symmetric Human Activity Recognition(MWHODL-SHAR)model.The major intention of the MWHODL-SHAR model lies in recognition of symmetric activities,namely jogging,walking,standing,sitting,etc.In the presented MWHODL-SHAR technique,the human activities data is pre-processed in various stages to make it compatible for further processing.A convolution neural network with an attention-based long short-term memory(CNNALSTM)model is applied for activity recognition.The MWHO algorithm is utilized as a hyperparameter tuning strategy to improve the detection rate of the CNN-ALSTM algorithm.The experimental validation of the MWHODL-SHAR technique is simulated using a benchmark dataset.An extensive comparison study revealed the betterment of theMWHODL-SHAR technique over other recent approaches.
基金supported by the National Natural Science Foundation of China(Grant No.41875113).
文摘The influences of human activity on regional climate over China have been widely reported and drawn great attention from both the scientific community and governments.This paper reviews the evidence of the anthropogenic influence on regional climate over China from the perspectives of surface air temperature(SAT),precipitation,droughts,and surface wind speed,based on studies published since 2018.The reviewed evidence indicates that human activities,including greenhouse gas and anthropogenic aerosol emissions,land use and cover change,urbanization,and anthropogenic heat release,have contributed to changes in the SAT trend and the likelihood of regional record-breaking extreme high/low temperature events over China.The anthropogenically forced SAT signal can be detected back to the 1870s in the southeastern Tibetan Plateau region.Although the anthropogenic signal of summer precipitation over China is detectable and anthropogenic forcing has contributed to an increased likelihood of regional record-breaking heavy/low precipitation events,the anthropogenic precipitation signal over China is relatively obscure.Moreover,human activities have also contributed to a decline in surface wind speed,weakening of monsoon precipitation,and an increase in the frequency of droughts and compound extreme climate/weather events over China in recent decades.This review can serve as a reference both for further understanding the causes of regional climate changes over China and for sound decision-making on regional climate mitigation and adaptation.Additionally,a few key or challenging scientific issues associated with the human influence on regional climate changes are discussed in the context of future research.
基金the National Natural Sci-ence Foundation of China(Grant No.42001139)the Second Ti-betan Plateau Scientific Expedition and Research Program(Grant No.2019QZKK0406)+1 种基金the National Natural Science Foundation of China(Grant No.42230510)the China Postdoctoral Science Foundation(Grant No.2020M670472).
文摘Investigating the spatiotemporal variation of human activity intensity and its determinants is a crucial basis for further revealing the mechanism of human-environment interaction and optimizing the human development mode.In this study,the human activity intensity on the Qinghai-Tibet Plateau(QTP)from 1990 to 2020 was measured based on the quantitative model of land use data and the actual regional background,and the under-lying natural and socioeconomic determinants were investigated using spatial econometric methods.The results demonstrate that(1)the human activity intensity in QTP has increased by 11.96%,and there are differences in different spatial scales;the areas with high human activity intensity are distributed in the Hehuang Valley where Xining City and its surrounding areas are located,as well as the One-River and Two-River Area where Lhasa City and surrounding areas are located.(2)Human activity intensity has significant positive spatial spillover,suggesting that local changes will cause changes in the same direction in adjacent areas.(3)The human activ-ity intensity in QTP is affected by various determinants.Concerning socioeconomic factors,the economic level has no significant impact on the human activity intensity in QTP,which differs from the general regional law.Both urbanization and traffic conditions have a significant positive effect,and the impact intensity continues to increase.Concerning natural factors,topographic relief has a significant positive effect;the impacts of temper-ature and vegetation coverage have changed from insignificant to a significant positive effect;the impacts of precipitation and river network density have not been verified;there is no linear relationship between altitude and human activity intensity in the entire QTP,while it exists in local regions.Finally,this study proposes three policy implications for the realization of a more harmonious human-environment relationship in QTP.
文摘Human Action Recognition(HAR)and pose estimation from videos have gained significant attention among research communities due to its applica-tion in several areas namely intelligent surveillance,human robot interaction,robot vision,etc.Though considerable improvements have been made in recent days,design of an effective and accurate action recognition model is yet a difficult process owing to the existence of different obstacles such as variations in camera angle,occlusion,background,movement speed,and so on.From the literature,it is observed that hard to deal with the temporal dimension in the action recognition process.Convolutional neural network(CNN)models could be used widely to solve this.With this motivation,this study designs a novel key point extraction with deep convolutional neural networks based pose estimation(KPE-DCNN)model for activity recognition.The KPE-DCNN technique initially converts the input video into a sequence of frames followed by a three stage process namely key point extraction,hyperparameter tuning,and pose estimation.In the keypoint extraction process an OpenPose model is designed to compute the accurate key-points in the human pose.Then,an optimal DCNN model is developed to classify the human activities label based on the extracted key points.For improving the training process of the DCNN technique,RMSProp optimizer is used to optimally adjust the hyperparameters such as learning rate,batch size,and epoch count.The experimental results tested using benchmark dataset like UCF sports dataset showed that KPE-DCNN technique is able to achieve good results compared with benchmark algorithms like CNN,DBN,SVM,STAL,T-CNN and so on.
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.
文摘Human-Computer Interaction(HCI)is a sub-area within computer science focused on the study of the communication between people(users)and computers and the evaluation,implementation,and design of user interfaces for computer systems.HCI has accomplished effective incorporation of the human factors and software engineering of computing systems through the methods and concepts of cognitive science.Usability is an aspect of HCI dedicated to guar-anteeing that human–computer communication is,amongst other things,efficient,effective,and sustaining for the user.Simultaneously,Human activity recognition(HAR)aim is to identify actions from a sequence of observations on the activities of subjects and the environmental conditions.The vision-based HAR study is the basis of several applications involving health care,HCI,and video surveillance.This article develops a Fire Hawk Optimizer with Deep Learning Enabled Activ-ity Recognition(FHODL-AR)on HCI driven usability.In the presented FHODL-AR technique,the input images are investigated for the identification of different human activities.For feature extraction,a modified SqueezeNet model is intro-duced by the inclusion of few bypass connections to the SqueezeNet among Fire modules.Besides,the FHO algorithm is utilized as a hyperparameter optimization algorithm,which in turn boosts the classification performance.To detect and cate-gorize different kinds of activities,probabilistic neural network(PNN)classifier is applied.The experimental validation of the FHODL-AR technique is tested using benchmark datasets,and the outcomes reported the improvements of the FHODL-AR technique over other recent approaches.
文摘Human Activity Recognition(HAR)from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications,video-based behavior monitoring,video classification,and indexing,patient monitoring,robotics,and behavior analysis.Although many techniques are available for HAR in video analysis tasks,most of them are not focusing on behavioral analysis.Hence,a new HAR system analysis the behavioral activity of a person based on the deep learning approach proposed in this work.The most essential aim of this work is to recognize the complex activities that are useful in many tasks that are based on object detection,modelling of individual frame characteristics,and communication among them.Moreover,this work focuses on finding out the human actions from various video resolutions,invariant human poses,and nearness of multi objects.First,we identify the key and essential frames of each activity using histogram differences.Secondly,Discrete Wavelet Transform(DWT)is used in this system to extract coefficients from the sequence of key-frames where the activity is localized in space.Finally,an Adaptive Weighted Flow Net(AWFN)algorithm is proposed in this work for effective video activity recognition.Moreover,the proposed algorithm has been evaluated by comparing it with the existing Visual Geometry Group(VGG-16)convolution neural networks for making performance comparisons.This work focuses on competent deep learning-based feature extraction to discriminate the activities for performing the classification accuracy.The proposed model has been evaluated with VGG-16 using a combination of regular UCF-101 activity datasets and also in very challenging Low-quality videos such as HMDB51.From these investigations,it is proved that the proposed AWFN approach gives higher detection accuracy of 96%.It is approximately 0.3%to 7.88%of higher accuracy than state-of-art methods.
文摘With the development of wireless technology, Frequency-Modulated Continuous Wave (FMCW) radar has increased sensing capability and can be used to recognize human activity. These applications have gained wide-spread attention and become a hot research area. FMCW signals reflected by target activity can be collected, and human activity can be recognized based on the measurements. This paper focused on human activity recognition based on FMCW and DenseNet. We collected point clouds from FMCW and analyzed them to recognize human activity because different activities could lead to unique point cloud features. We built and trained the neural network to implement human activities using a FMCW signal. Firstly, this paper presented recent reviews about human activity recognition using wireless signals. Then, it introduced the basic concepts of FMCW radar and described the fundamental principles of the system using FMCW radar. We also provided the system framework, experiment scenario, and DenseNet neural network structure. Finally, we presented the experimental results and analyzed the accuracy of different neural network models. The system achieved recognition accuracy of 100 percent for five activities using the DenseNet. We concluded the paper by discussing the current issues and future research directions.
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.
文摘The applicability of the laws of thermodynamics to processes on Earth is discussed and it is shown that the chemical thermodynamics provides the reasonable basis for predicting probable changes. The historical evolution of the planet is considered in the framework of the Harari approach;a civilization’s level is estimated by the Kardashev scale based on the amount of energy it is able to use. During a short historical interval (≈500 years), when the effect of biological evolution is imperceptible and the main changes on the planet are caused by human activity, two systems are considered: 1) a nonequilibrium inhabited planet and 2) a quasi-equilibrium hypothetical planet without people, which is accepted as a comparison system. It is shown that in response to the energy impact, the equilibrium of the hypothetical system with the primordial nature is disturbed and processes are initiated aimed to prevent further growth of energy production. In the case of a real planet, this implements changes preventing the uncontrolled activities of humans—the energy producers. Climate change, an increase in the number of natural disasters and epidemics can be recognized as a direct response of the planet, while changes in socio-economic relations, morality, demographic situation, new threats etc. can be considered as an indirect reaction to changing conditions of human beings. The latter results from the mutual correlation between the progress of society, on the one hand, and humanitarian and political processes, on the other. The role of renewable and non-renewable energy sources in evolution is taken into account. Obviously, it is better to take meaningful measures to achieve an acceptable balance now than to wait for the blind and extremely painful action of the laws of nature, which would lead to a reduction in the population.
文摘With the improvement of people’s living standards,the demand for health monitoring and exercise detection is increasing.It is of great significance to study human activity recognition(HAR)methods that are different from traditional feature extraction methods.This article uses convolutional neural network(CNN)algorithms in deep learning to automatically extract features of activities related to human life.We used a stochastic gradient descent algorithm to optimize the parameters of the CNN.The trained network model is compressed on STM32CubeMX-AI.Finally,this article introduces the use of neural networks on embedded devices to recognize six human activities of daily life,such as sitting,standing,walking,jogging,upstairs,and downstairs.The acceleration sensor related to human activity information is used to obtain the relevant characteristics of the activity,thereby solving the HAR problem.By drawing the accuracy curve,loss function curve,and confusion matrix diagram of the training model,the recognition effect of the convolutional neural network can be seen more intuitively.After comparing the average accuracy of each set of experiments and the test set of the best model obtained from it,the best model is then selected.
基金This work was supported by the Deanship of Scientific Research at King Khalid University through a General Research Project under Grant Number GRP/41/42.
文摘Nowadays,the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data.The video datasets are generated using cameras available in various devices that can be in a static or dynamic position and are referred to as untrimmed videos.Smarter monitoring is a historical necessity in which commonly occurring,regular,and out-of-the-ordinary activities can be automatically identified using intelligence systems and computer vision technology.In a long video,human activity may be present anywhere in the video.There can be a single ormultiple human activities present in such videos.This paper presents a deep learning-based methodology to identify the locally present human activities in the video sequences captured by a single wide-view camera in a sports environment.The recognition process is split into four parts:firstly,the video is divided into different set of frames,then the human body part in a sequence of frames is identified,next process is to identify the human activity using a convolutional neural network and finally the time information of the observed postures for each activity is determined with the help of a deep learning algorithm.The proposed approach has been tested on two different sports datasets including ActivityNet and THUMOS.Three sports activities like swimming,cricket bowling and high jump have been considered in this paper and classified with the temporal information i.e.,the start and end time for every activity present in the video.The convolutional neural network and long short-term memory are used for feature extraction of temporal action recognition from video data of sports activity.The outcomes show that the proposed method for activity recognition in the sports domain outperforms the existing methods.
基金National Natural Science Foundation of China(No. 70971021)
文摘With the emerging of sensor networks, research on sensor-based activity recognition has attracted much attention. Many existing methods cannot well deal with the cases that contain hundreds of sensors and their recognition accuracy is requisite to be further improved. A novel framework for recognizing human activities in smart home was presented. First, small, easy-to-install, and low-cost state change sensors were adopted for recording state change or use of the objects. Then the Bayesian belief network (BBN) was applied to conducting activity recognition by modeling statistical dependencies between sensor data and human activity. An edge-encode genetic algorithm (EEGA) approach was proposed to resolve the difficulties in structure learning of the BBN model under a high dimension space and large data set. Finally, some experiments were made using one publicly available dataset. The experimental results show that the EEGA algorithm is effective and efficient in learning the BBN structure and outperforms the conventional approaches. By conducting human activity recognition based on the testing samples, the BBN is effective to conduct human activity recognition and outperforms the naive Bayesian network (NBN) and multiclass naive Bayes classifier (MNBC).
基金This work is supported by NTU Presidential Postdoctoral Fellowship,"Adaptive Multimodal Learning for Robust Sensing and Recognition in Smart Cities"project fund,in Nanyang Technological University,Singapore.
文摘Device-free activity recognition plays a crucial role in smart building,security,and human–computer interaction,which shows its strength in its convenience and cost-efficiency.Traditional machine learning has made significant progress by heuristic hand-crafted features and statistical models,but it suffers from the limitation of manual feature design.Deep learning overcomes such issues by automatic high-level feature extraction,but its performance degrades due to the requirement of massive annotated data and cross-site issues.To deal with these problems,transfer learning helps to transfer knowledge from existing datasets while dealing with the negative effect of background dynamics.This paper surveys the recent progress of deep learning and transfer learning for device-free activity recognition.We begin with the motivation of deep learning and transfer learning,and then introduce the major sensor modalities.Then the deep and transfer learning techniques for device-free human activity recognition are introduced.Eventually,insights on existing works and grand challenges are summarized and presented to promote future research.
