Human Activity Patterns Research Articles

Human Activity and Motion Pattern Recognition within Indoor Environment Using Convolutional Neural Networks Clustering and Naive Bayes Classification Algorithms.

Human Activity Recognition (HAR) systems are designed to read sensor data and analyse it to classify any detected movement and respond accordingly. However, there is a need for more responsive and near real-time systems to distinguish between false and true alarms. To accurately determine alarm triggers, the motion pattern of legitimate users need to be stored over a certain period and used to train the system to recognise features associated with their movements. This training process is followed by a testing cycle that uses actual data of different patterns of activity that are either similar or different to the training data set. This paper evaluates the use of a combined Convolutional Neural Network (CNN) and Naive Bayes for accuracy and robustness to correctly identify true alarm triggers in the form of a buzzer sound for example. It shows that pattern recognition can be achieved using either of the two approaches, even when a partial motion pattern is derived as a subset out of a full-motion path.

The Mediation Role of Attitude toward Purchase in the Relationship between Country of Origin Image, Religiosity, Ethnocentrism, Animosity and Intentions to Purchase in the Egyptian Context

This research investigates the relationship between differences (ethnocentrism, country of origin image, religiosity and animosity) which may affect intentions to purchase. In doing so, it measures the effect of country of origin image, religiosity, animosity and ethnocentrism and their relationships with intentions to purchase Chinese fashion products in Egypt, through attitude toward purchase. Both of positivism and quantitative approach represent an organized method of research that combine the deductive logic and the empirical observations of individual behavior together in order to be able to note a group of causal laws that can be used for general prediction patterns of human activity. This study collects the data of the required variable by making a questionnaire. This questionnaire targeted a population that is represented in customers who purchase Chinese fashion products in Egypt. The analysis conducted is applied using SPSS 22.0 (Statistical Package for Social Sciences) and AMOS 24. Due to the infinite number of the targeted population, the sample of the study consisted of 384questionnaires gathered from Egyptian customers. The results of the analysis indicate that ethnocentrism and country of origin image have a positive impact on intentions to purchase and attitude toward purchase. While, religiosity and animosity have a negative impact on intentions to purchase and attitude toward purchase. Moreover, the results partially support the Mediation role of Attitude toward purchase in the relationship between (Ethnocentrism, Country of origin image, religiosity and Animosity) and Intentions to Purchase Chinese Fashion Products.

Reconstructing deforestation patterns in China from 2000 to 2019

Forests are important for the global carbon cycle, hydrothermal balance, and climate change. Human activities can exert a significant impact on forest ecosystems, thereby having the potential to alter their physical and chemical properties and thus affecting carbon, water, and heat budgets, and climate change. The historical reconstruction of the disturbance of global forests can help us understand the processes and patterns of human activities and global change. In this paper, we construct a deforestation prediction model using a Spearman correlation coefficient and implement the XGBoost method, using Python 3.6, for the reconstruction of deforestation intensity data from 2000 to 2019. Secondly, the selection of the driver indicators is done by using extreme difference regularization to unify the magnitude, and the potential deforestation area risk index is calculated in the form of equal weights. Finally, the actual deforestation data were used for optimization and validation. The model shows that the deforestation hotspots are mainly concentrated in the southern and southeastern regions of China and that there are large differences in deforestation in different provinces. In the future, the fine spatial and temporal patterns of deforestation in China during the historical period can be quantitatively reconstructed, which can provide some reference information for forest disaster prevention and forest management in China.

Learning Human Activity Patterns Using Clustered Point Processes With Active and Inactive States

Modeling event patterns is a central task in a wide range of disciplines. In applications such as studying human activity patterns, events often arrive clustered with sporadic and long periods of inactivity. Such heterogeneity in event patterns poses challenges for existing point process models. In this article, we propose a new class of clustered point processes that alternate between active and inactive states. The proposed model is flexible, highly interpretable, and can provide useful insights into event patterns. A composite likelihood approach and a composite EM estimation procedure are developed for efficient and numerically stable parameter estimation. We study both the computational and statistical properties of the estimator including convergence, consistency, and asymptotic normality. The proposed method is applied to Donald Trump’s Twitter data to investigate if and how his behaviors evolved before, during, and after the presidential campaign. Additionally, we analyze large-scale social media data from Sina Weibo and identify interesting groups of users with distinct behaviors.

Dinamika Pertumbuhan Kawasan Permukiman Kota Jambi

Urban Growth Dyanamics of Settlement Areas in Jambi City. The development of a city will follow trigger factors such as economy, service centers and population. This research tries to describe the growth of urban areas in Jambi City from 2000 to 2020 which is supported by several driving factors from human activity and movement patterns using time series image data from 2000, 2008, 2016 and 2020. The results show that there is a development in the urban area which leads to the Center Business District (CBD). Based on the growth area, there is an increase in the growth of the urban area which leads to expansion diffusion model with concentric shapes. Urban areas that always sustain growth are centered in the Central Business District: Pasar Jambi District, Jelutung District and Jambi Selatan District.

Dinamika Pertumbuhan Kawasan Permukiman Kota Jambi

Urban Growth Dyanamics of Settlement Areas in Jambi City. The development of a city will follow trigger factors such as economy, service centers and population. This research tries to describe the growth of urban areas in Jambi City from 2000 to 2020 which is supported by several driving factors from human activity and movement patterns using time series image data from 2000, 2008, 2016 and 2020. The results show that there is a development in the urban area which leads to the Center Business District (CBD). Based on the growth area, there is an increase in the growth of the urban area which leads to expansion diffusion model with concentric shapes. Urban areas that always sustain growth are centered in the Central Business District: Pasar Jambi District, Jelutung District and Jambi Selatan District.

Correcting the Saturation Effect in DMSP/OLS Stable Nighttime Light Products Based on Radiance-Calibrated Data

Nighttime light (NTL) products have become emerging instruments for studying human activity patterns in various applications, including demarcating urban areas, estimating the residential population, and monitoring the economic livelihood of cities. However, the deployment of such products, particularly the Defense Meteorological Satellite Program (DMSP)/Operational Line Scan (OLS) stable NTL data, is also subject to issues of data quality and methodological biases, including interannual inconsistency, saturation and blooming effect, and, in particular, the saturation problem, posing a challenge for further applications. This study proposed a novel saturation correction method based on regression model and radiance-calibrated NTL data (SARMRC) to correct the saturation effect in annual stable NTL data. The proposed method was applied to mainland China. The results show that SARMRC can effectively recover the light intensity distribution within the saturated areas of the stable NTL data. The corrected images were proven to be more spatially consistent with the reference National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) data and can better reflect the temporal development of cities compared to existing saturation correction methods (e.g., the index-based vegetation adjusted NTL urban index (VANUI) method and the interpolation method). The superior performance of SARMRC can be attributed to: 1) better utilization of both radiance-calibrated and stable NTL products and 2) the employment of training pixel selection and logarithmic model as well as double-year adjustment. The new method is expected to improve the data quality of DMSP/OLS stable NTL data for analyzing both local and regional socioeconomic activities.

Application of GIS to assess the accessibility of urban streets in Baghdad city

Accessibility has an important impact on shaping human activity patterns on all of the spatial scales. This study presented an evaluation of accessibility levels with private to commercial centers for three selected routes in Baghdad city. The study involved more than 45 days transport survey for private vehicles in Baghdad city using Global Positioning System (GPS) probe for recording indicators of traffic performance. Gravity model was used to measure accessibility index as an implementation of GIS-based model by using link geography and the spatial boundary of analysis in order to build route networks at three routes in Baghdad City, Bayaa intersection - Bab Al-Muatham intersection (Route 1), Bayaa intersection - Bab Al-Muatham intersection (Route 2) and 14 Ramadan Street - Bab Al-Muatham intersection (Route 3). It was found that Route 1 has the high accessibility index with 0.67 in compare with Route 2 and 3 (0.58 and 0.59), respectively. The reason that Route 1 had the highest accessibility index due to the high access point and low traffic volume as compared with the other two routes.

A Semi-Supervised Transfer Learning with Dynamic Associate Domain Adaptation for Human Activity Recognition Using WiFi Signals.

Human activity recognition without equipment plays a vital role in smart home applications, freeing humans from the shackles of wearable devices. In this paper, by using the channel state information (CSI) of the WiFi signal, semi-supervised transfer learning with dynamic associate domain adaptation is proposed for human activity recognition. In order to improve the CSI quality and denoising of CSI, we carried out missing packet filling, burst noise removal, background estimation, feature extraction, feature enhancement, and data augmentation in the data pre-processing stage. This paper considers the problem of environment-independent human activity recognition, also known as domain adaptation. The pre-trained model is trained from the source domain by collecting a complete labeled dataset of all of the CSI of human activity patterns. Then, the pre-trained model is transferred to the target environment through the semi-supervised transfer learning stage. Therefore, when humans move to different target domains, a partial labeled dataset of the target domain is required for fine-tuning. In this paper, we propose a dynamic associate domain adaptation called DADA. By modifying the existing associate domain adaptation algorithm, the target domain can provide a dynamic ratio of labeled dataset/unlabeled dataset, while the existing associate domain adaptation algorithm only allows target domains with the unlabeled dataset. The advantage of DADA is that it provides a dynamic strategy to eliminate different effects on different environments. In addition, we further designed an attention-based DenseNet model, or AD, as our training network, which is modified by an existing DenseNet by adding the attention function. The solution we proposed was simplified to DADA-AD throughout the paper. The experimental results show that for domain adaptation in different domains, the accuracy of human activity recognition of the DADA-AD scheme is 97.4%. It also shows that DADA-AD has advantages over existing semi-supervised learning schemes.

Integrating the Eigendecomposition Approach and k-Means Clustering for Inferring Building Functions with Location-Based Social Media Data

Understanding the relationship between human activity patterns and urban spatial structure planning is one of the core research topics in urban planning. Since a building is the basic spatial unit of the urban spatial structure, identifying building function types, according to human activities, is essential but challenging. This study presented a novel approach that integrated the eigendecomposition method and k-means clustering for inferring building function types according to location-based social media data, Tencent User Density (TUD) data. The eigendecomposition approach was used to extract the effective principal components (PCs) to characterize the temporal patterns of human activities at building level. This was combined with k-means clustering for building function identification. The proposed method was applied to the study area of Tianhe district, Guangzhou, one of the largest cities in China. The building inference results were verified through the random sampling of AOI data and street views in Baidu Maps. The accuracy for all building clusters exceeded 83.00%. The results indicated that the eigendecomposition approach is effective for revealing the temporal structure inherent in human activities, and the proposed eigendecomposition-k-means clustering approach is reliable for building function identification based on social media data.

Whole-brain propagating patterns in human resting-state brain activities

Repetitive propagating activities in resting-state brain activities have been widely observed in various species and regions. Because they resemble the preceding brain activities during tasks, they are assumed to reflect past experiences embedded in neuronal circuits. “Whole-brain” propagating activities may also reflect a process that integrates information distributed over the entire brain, such as visual and motor information. Here we reveal whole-brain propagating activities from human resting-state magnetoencephalography (MEG) and electroencephalography (EEG) data. We simultaneously recorded the MEGs and EEGs and estimated the source currents from both measurements. Then using our recently proposed algorithm, we extracted repetitive spatiotemporal patterns from the source currents. The estimated patterns consisted of multiple frequency components, each of which transiently exhibited the frequency-specific resting-state networks (RSNs) of functional MRIs (fMRIs), such as the default mode and sensorimotor networks. A simulation test suggested that the spatiotemporal patterns reflected the phase alignment of the multiple frequency oscillators induced by the propagating activities along the anatomical connectivity. These results argue that whole-brain propagating activities transiently exhibited multiple RSNs in their multiple frequency components, suggesting that they reflected a process to integrate the information distributed over the frequencies and networks.

Microbial risk assessment and mitigation options for wastewater treatment in Arctic Canada

Populations in Arctic Canada are strongly connected to, and draw sustenance from, the physical environment. Recreation and food harvesting locations, however, may be impacted by the basic wastewater treatment and disposal processes used in the region. Within these mixed socio-ecological systems, people may unknowingly be exposed to wastewater pathogens, either by direct contact or indirectly through activities resulting in exposure to contaminated locally harvested food. The objectives of this research are to estimate microbial health risks attributable to wastewater effluent exposure in Arctic Canada and evaluate potential mitigation options. A participatory quantitative microbial risk assessment (QMRA) approach was used. Specifically, community knowledge and information describing human activity patterns in wastewater-impacted environments was used with microbial water quality data to model a range of exposure scenarios and risk mitigation options. In several exposure scenario results, estimated individual annual risk of acute gastrointestinal illness exceeds a proposed tolerable target of 10−3. These scenarios include shore recreation and consumption of shellfish harvested near primary mechanical treatment plants at low tide, as well as travel in wetland portions of passive treatment sites during spring freshet. These results suggest that wastewater effluent exposures may be contributing to gastrointestinal illness in some Arctic communities. Mitigation strategies, including improved treatment and interventions aimed at deterring access to disposal areas reduce risk estimates across scenarios to varying degrees. Overall, well-designed passive systems appear to be the most effective wastewater treatment option for Arctic Canada in terms of limiting and managing associated microbial health risks. This research demonstrates a novel application of QMRA and provides science-based evidence to support public health, water, and sanitation decisions and investment in Arctic regions.

Revealing the relationship of topics popularity and bursty human activity patterns in social temporal networks

In social networks, how human activity patterns affect the popularity of topics has always been the focus of research. In this paper, a quantitative temporal analysis of the dynamics of topics popularity in Sina Weibo system was provided. Firstly, the popularity time series of 1167 topics were clustered into four clusters by K-Spectral Centroid (K-SC) clustering algorithm. Secondly, for each cluster, we calculated the exponents of topic popularity decay distribution α and the exponents of inter-activity time distribution β, respectively. Two interesting results were found: one is that the peak fraction F of topics popularity positively correlated with the topics popularity decay exponent α; the other is that bursty activity patterns in social network significantly affect topics popularity dynamics: there is a positive correlation between exponent α and exponent β. Finally, we proposed an extended SI (susceptible–infected) epidemic model with incorporate bursty human activity and verified the results by simulation.

Different patterns of human activities in nature during Covid-19 pandemic and African swine fever outbreak confirm direct impact on wildlife disruption

Implementation of various restrictions to eradicate viral diseases has globally affected human activity and subsequently nature. But how can the altered routines of human activity (restrictions, lockdowns) affect wildlife behaviour? This study compared the differences between human and wildlife occurrences in the study forest area with acreage of 5430.6 ha in 2018 (African swine fever outbreak, complete entrance ban), 2019 (standard pattern) and 2020 (COVID-19 restrictions) during the breeding season. The number of visitors was lower by 64% in 2018 (non-respecting of the entry ban by forest visitors) compared to standard 2019, while in 2020, the number of visitors increased to 151%. In the COVID-19 period, distinct peaks in the number of visitors were observed between 8–11 AM and 4–7 PM. The peaks of wildlife activity were recorded between 4–7 AM and 9–12 PM. Animals avoided the localities that were visited by humans during the people-influenced time (24 h after people visit), which confirmed the direct negative impact of human activities on wildlife.

When makes you unique: Temporality of the human brain fingerprint.

The extraction of “fingerprints” from human brain connectivity data has become a new frontier in neuroscience. However, the time scales of human brain identifiability are still largely unexplored. We here investigate the dynamics of brain fingerprints along two complementary axes: (i) What is the optimal time scale at which brain fingerprints integrate information and (ii) when best identification happens. Using dynamic identifiability, we show that the best identification emerges at longer time scales; however, short transient “bursts of identifiability,” associated with neuronal activity, persist even when looking at shorter functional interactions. Furthermore, we report evidence that different parts of connectome fingerprints relate to different time scales, i.e., more visual-somatomotor at short temporal windows and more frontoparietal-DMN driven at increasing temporal windows. Last, different cognitive functions appear to be meta-analytically implicated in dynamic fingerprints across time scales. We hope that this investigation will advance our understanding of what makes our brains unique.

Duplicities: the false promise of Asian Studies

ABSTRACT The rise of Asian states—particularly China and India—and their search for energy, raw materials, and markets has spurred talk of a “Global Asia.” Though this has spawned a vast literature, the complexities of language and research environments and the absence of a complex grid of scholarly exchanges—translations, collaborative exchanges, comparative analysis—has meant that these studies have not challenged reigning conceptions of a broad geocultural area and studies of individual states or subregions continue to emphasize their exceptionalism. Though inter-referentiality was projected as a strategy to break out of the “intellectual claustrophobia” of area studies, the continued acceptance of Asia as a cartographic space marginalizes the very global linkages that are salient aspects of the present epoch. Hence area studies scholarship continue largely to obscure the global reach of state and private agencies and of their diasporas as these seek to project elite views and understandings of their states overseas. Rather than taking cartographic units as epistemological fields, we need to chart patterns of human activity to historicize spatial designations.

Semantic Enhancement of Human Urban Activity Chain Construction Using Mobile Phone Signaling Data

Data-driven urban human activity mining has become a hot topic of urban dynamic modeling and analysis. Semantic activity chain modeling with activity purpose provides scientific methodological support for the analysis and decision-making of human behavior, urban planning, traffic management, green sustainable development, etc. However, the spatial and temporal uncertainty of the ubiquitous mobile sensing data brings a huge challenge for modeling and analyzing human activities. Existing approaches for modeling and identifying human activities based on massive social sensing data rely on a large number of valid supervised samples or limited prior knowledge. This paper proposes an effective methodology for building human activity chains based on mobile phone signaling data and labeling activity purpose semantics to analyze human activity patterns, spatiotemporal behavior, and urban dynamics. We fully verified the effectiveness and accuracy of the proposed method in human daily activity process construction and activity purpose identification through accuracy comparison and spatial-temporal distribution exploration. This study further confirms the possibility of using big data to observe urban human spatiotemporal behavior.

Impacts of dams and land-use changes on hydromorphology of braided channels in the Lhasa River of the Qinghai-Tibet Plateau, China

Among braided rivers developed on the Qinghai-Tibet Plateau of China at very high elevations (>3,500 m), the middle and lower reaches of the Lhasa River have been affected by comprehensive human activities mainly involving dam construction, urbanization, farming, afforestation, and mining. In the current study, the impacts of these human activities on hydrology and morphology of the four braided reaches downstream of a cascaded of two dams are investigated. The study period was divided into 1985–2006 (P1), 2006–2013 (P2), and 2013–2019 (P3), representing the natural and changed flow regimes by dams. Using available daily discharge data at two stations within the four braided reaches, dam-induced hydrological alteration was analyzed based on the indicators of hydrologic alteration and range of variability approach and key discharge proxies were calculated. Remotely sensed images also were selected in the three periods and morphological metrics extracted from them were compared for the four reaches among these periods. Attenuated hydrological regimes were found for only two reaches. The total channel width (Wc) and braiding intensity (BIt) followed different temporal trends among the four reaches. Annual average shift rates of the main channel in the four reaches were higher in the short (P2–P3) than in the long (P1–P2 and P1–P3) periods. The longitudinal changes of Wc and the number of channels did not have any identifiable trend among the four reaches. By linking the morphological changes to quantified spatial and temporal patterns of various human activities, it was found that (1) the two dams had insignificant impact on channel morphology, suggesting that the studied braided river might have a short relaxation time and (2) the evolutional trajectories of morphological changes in most of the four reaches were similar, suggesting that temporal trends of morphological changes due to complex human activities are not affected by the different physiographic settings of the reaches. Continuous exploitation of the valley area requires comprehensive river management strategies for coordinating various human activities.

Insect-mediated apparent competition between mammals in a boreal food web

While the important role of animal-mediated interactions in the top-down restructuring of plant communities is well documented, less is known of their ensuing repercussions at higher trophic levels. We demonstrate how typically decoupled ecological interactions may become intertwined such that the impact of an insect pest on forest structure and composition alters predator-prey interactions among large mammals. Specifically, we show how irruptions in a common, cyclic insect pest of the boreal forest, the spruce budworm (Choristoneura fumiferana), modulated an indirect trophic interaction by initiating a flush in deciduous vegetation that benefited moose (Alces alces), in turn strengthening apparent competition between moose and threatened boreal caribou (Rangifer tarandus caribou) via wolf (Canis lupus) predation. Critically, predation on caribou postoutbreak was exacerbated by human activity (salvage logging). We believe our observations of significant, large-scale reverberating consumer-producer-consumer interactions are likely to be common in nature.

A novel meteorological method to classify wintertime cold-air pool events

Cold-air pools (CAPs) are common in mountain valleys throughout the world (e.g., western North America, Himalayas, Alps, etc.) during winter months. Weak surface winds, cold temperatures, high humidity, and snow cover presence are common characteristics of CAPs, and in populated areas there is an increase in air pollution concentrations. Previous methods for identifying CAP events and determining their strength often rely on a combination of, radiosonde data, air pollution concentrations, and/or surface meteorological datasets. Ambient air pollution concentrations vary by location based on the local emissions sources and continually change due to regulations and human activity patterns, therefore they are unreliable for consistent CAP quantification. Here, the bulk atmospheric stability is calculated as the valley heat deficit (VHD) using radiosonde data for 12 locations in the western U.S. over 16 winters. A new CAP classification method is developed and compared to three existing CAP classification methods. Results indicate that the new method agrees well with existing approaches but provides a more robust CAP classification because it is solely based on meteorology and not air quality. For all locations, 00Z (afternoon/early evening) radiosondes account for roughly 8–41% of all CAP occurrences (12Z and 00Z), independent of the method used. Meaning that the stable boundary layer persists throughout the daytime in these cases often leading to persistent CAP events (PCAP). While PCAP length varies across locations, they are a similar order of magnitude because synoptic conditions that span the entire western U.S. govern CAP onset and PCAP length. Additionally, several locations (e.g., Reno, Elko, Spokane, Riverton, and Grand Junction) release radiosondes from a site located at an elevation above the valley floor, often underestimating the bulk atmospheric stability by more than 30%. A method to incorporate surface station data in the bulk atmospheric stability calculation is given to reduce this underestimation.