Human Activity Recognition Based on Frequency-Modulated Continuous Wave and DenseNet

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.

用于学习和解释行人预期行为的群体交互场

Anticipating others’actions is innate and essential in order for humans to navigate and interact well with others in dense crowds.This ability is urgently required for unmanned systems such as service robots and self-driving cars.However,existing solutions struggle to predict pedestrian anticipation accurately,because the influence of group-related social behaviors has not been well considered.While group relationships and group interactions are ubiquitous and significantly influence pedestrian anticipation,their influence is diverse and subtle,making it difficult to explicitly quantify.Here,we propose the group interaction field(GIF),a novel group-aware representation that quantifies pedestrian anticipation into a probability field of pedestrians’future locations and attention orientations.An end-to-end neural network,GIFNet,is tailored to estimate the GIF from explicit multidimensional observations.GIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagation and graph attention that is adaptive to the group size and dynamic interaction states.The experimental results show that the GIF effectively represents the change in pedestrians’anticipation under the prominent impact of group behaviors and accurately predicts pedestrians’future states.Moreover,the GIF contributes to explaining various predictions of pedestrians’behavior in different social states.The proposed GIF will eventually be able to allow unmanned systems to work in a human-like manner and comply with social norms,thereby promoting harmonious human-machine relationships.

Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector Machines

Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers.In this paper,we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance.Research have mostly focused the problem of human detection in thin crowd,overall behavior of the crowd and actions of individuals in video sequences.Vision based Human behavior modeling is a complex task as it involves human detection,tracking,classifying normal and abnormal behavior.The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e.,fill hole inside objects algorithm.Human detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction algorithm.The classification task is achieved using binary and multi class support vector machines.The proposed technique is validated through accuracy,precision,recall and F-measure metrics.

Study on Local Optical Flow Method Based on YOLOv3 in Human Behavior Recognition

In the process of human behavior recognition, the traditional dense optical flow method has too many pixels and too much overhead, which limits the running speed. This paper proposed a method combing YOLOv3 (You Only Look Once v3) and local optical flow method. Based on the dense optical flow method, the optical flow modulus of the area where the human target is detected is calculated to reduce the amount of computation and save the cost in terms of time. And then, a threshold value is set to complete the human behavior identification. Through design algorithm, experimental verification and other steps, the walking, running and falling state of human body in real life indoor sports video was identified. Experimental results show that this algorithm is more advantageous for jogging behavior recognition.

An Event Alarm System Based on Single and Group Human Behavior Analysis

Due to the increasing demand for security, the development of intelligent surveillance systems has attracted considerable attention in recent years. This study aims to develop a system that is able to identify whether or not the people need help in a public place. Different from previous work, our work considers not only the behaviors of the target person but also the interaction between him and nearby people. In the paper, we propose an event alarm system which can detect the human behaviors and recognize the happening event through integrating the results generated from the single and group behavior analysis. Several new effective features are proposed in the study. Besides, a mechanism capable of extracting one-to-one and multiple-to-one relations is also developed. Experimental results show that the proposed approach can correctly detect human behaviors and provide the alarm messages when emergency events occur.

Biological behaviors of two novel syngeneic human osteosarcoma cell lines

Objective To characterize and compare the different biological behaviors of two novel human osteosarcoma cell lines,Zos and Zos-M,established respectively from the primary site and the skip metastasis of an osteosarcoma patient.Methods Two

Origin of Traditional Chinese Medicine in Cuba in the 19th Century from Its Main Exponents and Some Notable Medical Descendants in the 20th Century

The Cuban people are made up of three major migratory currents, the Chinese are one of them. They brought their culture, the methods, and procedures of traditional Chinese medicine(TCM) in the 19th century. Few were able to return and so they created families in Cuba;some of their descendants dedicated themselves to medicine. In order to investigate the practices that were predecessors of TCM in Cuba in the 19th century, a qualitative phenomenological research was carried out, reviewing what was published by various sources, applying documentary analysis, logical historical analysis, abstraction, synthesis, and systematization of the results on the regularities of the work and human behavior of Chinese doctors in the Cuban 19th and 20th centuries. This made it possible to identify six Chinese doctors in the 19th century in Cuba who gave rise to the beginning of some practices of TCM in Cuba, and five from the 20th century, descendants of coolies who dedicated themselves to other specialties of medicine. It was found that despite their geographical and time disperse, they were all notorious for their outstanding professional and human behavior, with a trail of accumulated successes in achieving “almost the impossible” with the patient. They have left their mark on Cuban culture.

Behavior Recognition of the Elderly in Indoor Environment Based on Feature Fusion of Wi-Fi Perception and Videos

With the intensifying aging of the population,the phenomenon of the elderly living alone is also increasing.Therefore,using modern internet of things technology to monitor the daily behavior of the elderly in indoors is a meaningful study.Video-based action recognition tasks are easily affected by object occlusion and weak ambient light,resulting in poor recognition performance.Therefore,this paper proposes an indoor human behavior recognition method based on wireless fidelity(Wi-Fi)perception and video feature fusion by utilizing the ability of Wi-Fi signals to carry environmental information during the propagation process.This paper uses the public WiFi-based activity recognition dataset(WIAR)containing Wi-Fi channel state information and essential action videos,and then extracts video feature vectors and Wi-Fi signal feature vectors in the datasets through the two-stream convolutional neural network and standard statistical algorithms,respectively.Then the two sets of feature vectors are fused,and finally,the action classification and recognition are performed by the support vector machine(SVM).The experiments in this paper contrast experiments between the two-stream network model and the methods in this paper under three different environments.And the accuracy of action recognition after adding Wi-Fi signal feature fusion is improved by 10%on average.

E-Mobility in Urban Areas and the Impact of Parking Organisation

In this paper we explore the preconditions and requirements in order to enable the renewal of the vehicle fleet towards e-cars without weakening eco-mobility(public transport,biking,walking).We follow a linked approach of arranging charging infrastructure and regulating the parking spaces.We analyze the results of this approach by modeling different scenarios for the case study city of Vienna with the LUTI(land-use transport interaction)model MARS(Metropolitan Activity Relocation Simulator).Four different policy scenarios are modeled and the results compared.We look at changes in transport behavior(modal split and vehicle kilometers),the emissions and the impact on public transport ridership.

Image Semantic Segmentation Approach for Studying Human Behavior on Image Data

Image semantic segmentation is an essential technique for studying human behavior through image data.This paper proposes an image semantic segmentation method for human behavior research.Firstly,an end-to-end convolutional neural network architecture is proposed,which consists of a depth-separable jump-connected fully convolutional network and a conditional random field network;then jump-connected convolution is used to classify each pixel in the image,and an image semantic segmentation method based on convolu-tional neural network is proposed;and then a conditional random field network is used to improve the effect of image segmentation of hu-man behavior and a linear modeling and nonlinear modeling method based on the semantic segmentation of conditional random field im-age is proposed.Finally,using the proposed image segmentation network,the input entrepreneurial image data is semantically segmented to obtain the contour features of the person;and the segmentation of the images in the medical field.The experimental results show that the image semantic segmentation method is effective.It is a new way to use image data to study human behavior and can be extended to other research areas.

Seeker optimization algorithm:a novel stochastic search algorithm for global numerical optimization

A novel heuristic search algorithm called seeker op- timization algorithm （SOA） is proposed for the real-parameter optimization. The proposed SOA is based on simulating the act of human searching. In the SOA, search direction is based on empir- ical gradients by evaluating the response to the position changes, while step length is based on uncertainty reasoning by using a simple fuzzy rule. The effectiveness of the SOA is evaluated by using a challenging set of typically complex functions in compari- son to differential evolution （DE） and three modified particle swarm optimization （PSO） algorithms. The simulation results show that the performance of the SOA is superior or comparable to that of the other algorithms.

Cognitive work analysis to comprehend operations and organizations in the mining industry

Complex industrial systems, including mining, have a prominent challenge in understanding the interrelationship among the cognitive processes, working environment and available equipment. The concept of cognitive work analysis(CWA) transcends the traditional analytic methods of evaluating human tasks solely based on perceptual and physical traits, and rather implements the notions of behavioral and cognitive awareness indispensable for the intricacy of modern technology. In the last few decades, academic and industrial settings employ this type of analysis to set a suitable standard for a system's safety feasibility, and as a result reduce human-based errors. This research paper analyzes current CWA methods and proposes a five-level quantification model portraying the overall cognitive quality of a mining operation.

Our mindset, our behavior and our future

To illustrate how the universality of climate change is exhibited in radically different specifics,Kalamazoo,Michigan’s“100-year flood plain”which has been flooded three or four times in the past several years is offered as an immediate example.The county’s general topography and very complex watersheds are described,noting the similarity between this microcosm and giant riparian systems.China’s enormous data collection and analysis system founded on a magnificent recursive feedback loop is described.The parallel structure of human cognition as an inherited psycho-biological recursive feedback loop as the structure of all human cognition and learning is described with reference to how infants actually learn their native language.A brief summary of the critical role of China’s“Three Teachings”(Confucianism,Daoism and Buddhism)in fostering adaptation to nature is proposed in contrast to the Western preference for manipulating nature to fit human comfort.Practicing traditional modes of“meditation”is urged as a pathway towards a brighter future for both humanity and the nature.Coopting specialists in publicizing and advertising is required to help change the human narrative.

A Theoretical Approach to the Effective Decision-Making Process

This research develops and elaborates studies done for a contribution to the 2019 PIC International Conference 2019 in Malta, about the decision-making process. Decision-making is the act of choosing between two or more courses of action. In the wider process of problem-solving, decision-making involves choosing between possible solutions to a problem, and these decisions can be made through either an intuitive or reasoned process, or a combination of the two. The study of decision-making processes, to be understood as the role of human factors, becomes particularly interesting in complex organizations. This research aims to analyze how an effective team, within organizations, can develop a more correct and effective decision-making, in order to get an optimal solution, overcoming the typical uncertainty. The paper describes the point of departure of decision in complex, time-pressured, uncertain, ambiguous and changing environments. The use of a leading case (the Tenerife air accident, 1977), will lead us to the desired results, i.e. to demonstrate how an effective decisional process, including team dynamics, can be useful to reduce the risk, present in all decisions, and reduce errors. The case of Tenerife air disaster, confirm our research. In that case, in fact, the group dynamics prove not to have worked. Thus, we can state that if a team approach had been followed instead of a more individual one, the results would probably have been different. The central belief of the research, is that classic decision theory could benefit from a team approach, which reduces the risk that a decision may lead to undesirable consequences. As demonstrated with the case study, within organizations, the decision-making is not a solitary action. Decisions, in fact, are made within a team and in order to be able to function effectively in a group, and manage group situations, there are essential skills. The team can then become a resource for the decisional process and problem solving, but it is necessary to understand the dynamics.

Earth surface processes and their effects on human behavior in monsoonal China during the Pleistocene-Holocene epochs

There is a wide diversity of landforms in China. The topography of three major ter- races, decreasing in height stepwise from west to east, was formed by the early Miocene. With the commencement of the Great Northern Hemisphere Glaciations （GHGs） and the glacial-interglacial cycles in the Pleistocene, thick loess deposits accumulated in north China, and fluvial terraces were formed and lakes expanded and contracted in eastern and central China. The earliest evidence of hominins in China is dated to ~1.7 Ma; they occupied the monsoon-dominated region for a long interval, until the late Pleistocene, ~50 ka. In this study, we investigated a large area rich in the relics and artifacts of early man. The results indicate that the early humans occupied riverine areas, especially medium-sized fluvial basins, and lake shores. Even in the relatively recent geological past, the occupation and abandonment of settlements were directly forced by the shifting of sand dune fields in the desert-loess transi- tional zone, which in turn was closely associated with variations in the monsoon climate and vegetation patterns. Our observations indicate that landforms were one of the main determi- nants of early human behavior, in that loess tableland, large alluvial plains, desert-Gobi areas, and the Tibetan Plateau, were not suitable environments for early human settlement. We infer that the early humans in China adapted their behavior to specific landforms and landform processes. The monsoon climate, which shapes the large-scale step-like pattern of fluvial landforms, promotes vegetation coverage and dominates soil formation, provides a crucial context for early human adaptation. The adaptation of early humans to earth surface proc- esses in East Asia is investigated for the first time in this study. Future investigations will provide further information that will increase our understanding of the linkage between early human behavior and landform processes in East Asia.

Integrated design of transport infrastructure and public spaces considering human behavior:A review of state-of-the-art methods and tools

In order to achieve holistic urban plans incorporating transport infrastructure,public space and the behavior of people in these spaces,integration of urban design and computer modeling is a promising way to provide both qualitative and quantitative support to decisionmakers.This paper describes a systematic literature review following a four-part framework.Firstly,to understand the relationship of elements of transport,spaces,and humans,w e review policy and urban design strategies for promoting positive interactions.Secondly,we present an overview of the integration methods and strategies used in urban design and policy discourses.Afterward,metrics and approaches for evaluating the effectiveness of integrated plan alternatives are reviewed.Finally,this paper gives a review of state-of-the-art tools with a focus on seven com puter simulation paradigms.This article explores mechanisms underlying the complex system of transport,spaces,and humans from a multidisciplinary perspective to provide an integrated toolkit for designers,planners,modelers and decision-m akers with the current methods and their challenges.

The modelling and application of cross-scale human behavior in realizing the shop-floor digital twin[version 1;peer review:1 approved with reservations,1 not approved]

The digital twin shop-floor has received much attention from the manufacturing industry as it is an important way to upgrade the shop-floor digitally and intelligently.As a key part of the shop-floor,humans'high autonomy and uncertainty leads to the difficulty in digital twin modeling of human behavior.Therefore,the modeling system for cross-scale human behavior in digital twin shop-floors was developed,powered by the data fusion of macro-behavior and micro-behavior virtual models.Shop-floor human macro-behavior mainly refers to the role of the human and their real-time position.Shop-floor micro-behavior mainly refers to real-time human limb posture and production behavior at their workstation.In this study,we reviewed and summarized a set of theoretical systems for cross-scale human behavior modeling in digital twin shop-floors.Based on this theoretical system,we then reviewed modeling theory and technology from macro-behavior and micro-behavior aspects to analyze the research status of shop-floor human behavior modeling.Lastly,we discuss and offer opinion on the application of cross-scale human behavior modeling in digital twin shop-floors.Cross-scale human behavior modeling is the key for realizing closed-loop interactive drive of human behavior in digital twin shop-floors.

Emergency evacuation in a supermarket during a terrorist attack: towards a possible modelling of the influence of affordances on the evacuation behavior of agents in a complex virtual environment

During a terrorist attack on a supermarket,the use of emergency exits is essential for effective evacuation and saving lives.However,people tend to ignore emergency situations.This behavior can lengthen evacuation times,endanger individuals,and even prove fatal.In this context,we conducted a series of experiments to explore the links between cognition and the dynamics of human capabilities in a complex and changing environment.In a series of behavioral experiments and computer simulations,we found that active guidance by green flashing lights at emergency exits impacts the behavior of individuals in an emergency evacuation situation in a supermarket;this tested our hypothesis that changing the environment in turn changes the evacuation behavior of individuals.We also show that environmental modification can help in decision-making in an emergency situation.Furthermore,the results of computer simulations support a possible modeling of the influence of affordances on the evacuation behavior of agents in a complexvirtual environment.

Research on recognition algorithm for gesture page turning based on wireless sensing

When a human body moves within the coverage range of Wi-Fi signals,the reflected Wi-Fi signals by the various parts of the human body change the propagation path,so analysis of the channel state data can achieve the perception of the human motion.By extracting the Channel State Information(CSI)related to human motion from the Wi-Fi signals and analyzing it with the introduced machine learning classification algorithm,the human motion in the spatial environment can be perceived.On the basis of this theory,this paper proposed an algorithm of human behavior recognition based on CSI wireless sensing to realize deviceless and over-the-air slide turning.This algorithm collects the environmental information containing upward or downward wave in a conference room scene,uses the local outlier factor detection algorithm to segment the actions,and then the time domain features are extracted to train Support Vector Machine(SVM)and eXtreme Gradient Boosting(XGBoost)classification modules.The experimental results show that the average accuracy of the XGBoost module sensing slide flipping can reach 94%,and the SVM module can reach 89%,so the module could be extended to the field of smart classroom and significantly improve speech efficiency.

TransCode:Uncovering COVID-19 transmission patterns via deep learning

Background The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale,especially in densely populated regions.In this study,we aim to discover such fine-scale transmission patterns via deep learning.Methods We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors.First,in Hong Kong,China,we construct the mobility trajectories of confirmed cases using their visiting records.Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution.Integrating the spatial and temporal information,we represent the TransCode via spatiotemporal transmission networks.Further,we propose a deep transfer learning model to adapt the TransCode of Hong Kong,China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises:New York City,San Francisco,Toronto,London,Berlin,and Tokyo,where fine-scale data are limited.All the data used in this study are publicly available.Results The TransCode of Hong Kong,China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns(e.g.,the imported and exported transmission intensities)at the district and constituency levels during different COVID-19 outbreaks waves.By adapting the TransCode of Hong Kong,China to other data-limited densely populated metropolises,the proposed method outperforms other representative methods by more than 10%in terms of the prediction accuracy of the disease dynamics(i.e.,the trend of case numbers),and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level.Conclusions The fine-scale transmission patterns due to the metapopulation level mobility(e.g.,travel across different districts)and contact behaviors(e.g.,gathering in social-economic centers)are one of the main contributors to the rapid spread of the virus.Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions.