Context-aware activity prediction using human behavior pattern in real smart home environments

Prediction of humans’ behaviors during a disaster: The Behavioral Pattern during Disaster Indicator (BPDI)

SummaryAs human activities in mobile environments are facing an ever‐increasing range of data, it is of great research importance to explore in depth the salient features of data that discriminate human behavior patterns and socio‐demographic factors. However, human activity behavior that consists of a series of complex spatio‐temporal activity processes is difficult to model. In this article, we develop a framework to perform activity behavior pattern mining and recognition, and the proposed framework has been applied to the American Time Use Survey to explore representative activity behavior patterns and socio‐demographic factors. The main contributions are as follows: (1) A method of activity behavior similarity is presented based on daily activities and activity sequences. (2) An activity sequence similarity algorithm with is proposed by line segment tree, greedy algorithm, and dynamic programming. (3) Representative activity behavior patterns and socio‐demographic factors are derived by clustering analysis and mining. (4) The activity behavior pattern is recognized by activity behavior features or socio‐demographic features. Through the experiments, we find that different daily activity behavior patterns are associated with specific socio‐demographic factors.

Mining human behaviors has always been an important subarea of Data Mining. While it provides empirical evidences to psychological/behavioral studies, it also builds the foundation of various big-data systems, which rely heavily on the prediction of human behaviors. In recent years, the ubiquitous spreading of mobile phones and the massive amount of spatio-temporal data collected from them make it possible to keep track of the daily commute behaviors of mobile subscribers and further conduct routine mining on them. In this article, we propose to model mobile subscribers’ daily commute behaviors by three levels: location trajectory, one-day pattern, and routine pattern. We develop the model Spatio-Temporal Routine Mining Model (STRMM) to characterize the generative process between these three levels. From daily trajectories, the STRMM model unsupervisedly extracts spatio-temporal routine patterns that contain two aspects of information: (1) How people’s typical commute patterns are. (2) How much their commute behaviors vary from day to day. Compared to traditional methods, STRMM takes into account the different degrees of behavioral uncertainty in different timespans of a day, yielding more realistic and intuitive results. To learn model parameters, we adopt Stochastic Expectation Maximization algorithm. Experiments are conducted on two real world datasets, and the empirical results show that the STRMM model can effectively discover hidden routine patterns of human commute behaviors and yields higher accuracy results in trajectory prediction task.

Introduction : In this research, we investigate whether chaotic phenomena (Chaos Theory) regulate human brain physiological and pathological behavioral patterns (BP). Methods : Modeling the six basic neurotransmitters of Central Nervous System (CNS), that is to say Dopamine (DA), Serotonin (SE), Noradrenalin (NE), I³-aminobutiric acid (GABA), Glutamate (Glu) and Acetylcholine(Ach), a set of six first order differential equations have been developed and studied in phase-space. Results : The elementary equilibrium points in three (3-DS) and six dimensional (6-DS) phase portrait analysis, include attractors, saddles and repellors. Furthermore, it has been studied the 3-D phase-space of DA, SE and NE. Conclusions : Attractors indicate a stable equilibrium point which corresponds to the most stable behavioral pattern, while the saddles represent a occasional unstable behavioral pattern and finally the repellors correspond to an unstable dynamic system of a totally disorganized BP.. Among other mechanisms, chaotic phenomena seem to regulate in a particular way the CNS basic neurotransmitters resulting in a huge number of different theoretical BP. The implementation in 3-D phase space provides a different view of Psychosis, Bipolar Disorder and Depression. Further research is needed so as to establish the predictive and therapeutic value of this theoretical approach of Human Behavior.

Work-related stress and burnout among physicians are of increasing relevance. The aim of this study was to investigate work-related behavior and experience patterns and predictors of mental health of physicians working in medical practice in Germany. We surveyed a stratified, random sample of 900 physicians from different specialties. The questionnaire included the standardized instruments Work-related Behavior and Experience Pattern (AVEM) and the Short Form-12 Health Survey (SF-12). Only one third of physicians reported high or very high general satisfaction with their job, but 64% would choose to study medicine again. Only 18% of physicians presented a healthy behavior and experience pattern. Almost 40% presented a pattern of reduced motivation to work, 21% were at risk of overexertion, and 22% at risk for burnout. Willingness to study medicine again, fulfilled job expectations, professional years, marital status, and behavior patterns were significant predictors of mental health and accounted for 35.6% of the variance in mental health scores. Job-related perceptions also had a significant effect on burnout. The strong influence of work-related perceptions suggests a need for realistic expectation management in medical education, as well as support in stress management and coping strategies during medical training.

Twenty-four hour activity budgets and patterns of behavior in captive ocelots ( Leopardus pardalis)

As Internet use has proliferated, Web-based learning systems have become more and more popular. Numerous researchers have spent a great deal of effort to facilitate the promotion of high quality Web-based learning environments, such as intelligent Web-based learning systems and adaptive learning. To facilitate such researches, students' behavioral patterns must be observed and experimentally analyzed. However, building a Web-based learning system and the requisite collecting of behavioral patterns usually takes a great deal of time and effort. To solve this problem, this paper proposes a learning behavioral model based on Colored Petri Nets (CPN) to model and generate students' behavioral patterns. To verify the viability of the proposed model, this paper compares actual data collected from elementary school students with the behavioral pattern generated by the proposed model. The results prove: (1) The generated behavioral pattern approaches actual student behavior; (2) The generated behavioral pattern serves as adequate test data to test whether the predicted learning content of an intelligent e-learning system is appropriate; and (3) The proposed model is capable of recommending the appropriate learning content for students utilizing e-learning systems.

Knowing the behavioral patterns of city residents is of great value in formulating and adjusting urban planning strategies, such as urban road planning, urban commercial development, and urban pedestrian flow control. Based on the high penetration rate of cell phones, it is possible to indirectly understand the behavior of city residents based on the call records of users. However, the behavioral patterns of large‐scale users over a long period of time can present characteristics such as large dispersion, difficult to discover patterns, and difficult to explain behavioral patterns. In this paper, we design and implement a human behavior pattern analysis system based on massive mobile communication data based on serial data modeling method and visual analysis technology. For the problem that it is difficult to capture the behavioral patterns of residents in cities in call records, this paper constructs base station trajectories based on users’ cell phone call records and uses users’ long‐time base station trajectories to mine users’ potential behavioral patterns. Since users with similar activity characteristics will exhibit similar base station trajectories, this paper focuses on the similarity between text sequences and base station trajectory sequences and combines the word embedding method in natural language processing to build a Cell2vec model to identify the semantics of base stations in cities. In order to obtain the group behavior patterns of users from the base station trajectories of group users, a user clustering method based on users’ regional mobile preferences is proposed, and the results are projected using the Stochastic Neighbor Embedding (t‐SNE) algorithm to expose the clustering features of large‐scale cell phone users in the low‐dimensional space. To address the problem that user behavior patterns are difficult to interpret, a visual analysis model with group as well as regional semantics is designed for the spatial and temporal characteristics of user behavior. Among them, the clustering model uses the distance between scatter points to map the similarity between users, which helps analysts to explore the behavioral characteristics of group users.

Lithium chloride, cholecystokinin and meal patterns: Evidence that cholecystokinin suppresses meal size in rats without causing malaise

Metopic synostosis is a condition in which the metopic suture of the human cranium fuses prematurely and may be related to poor behavioral inhibition leading to behaviors commonly associated with Attention-Deficit Hyperactivity Disorder (ADHD). The purpose of this project was to examine the behavior patterns among children with a history of metopic synostosis. It was hypothesized that children with a history of metopic synostosis would exhibit many of the same behavioral patterns associated with ADHD. It was also hypothesized that children with a history of simple synostosis not involving the metopic suture would not evidence this type of behavioral pattern. In order to test these hypotheses, the behavior of three groups of children was compared including (1) children who had a history of metopic synostosis (M= 7.63 years, SD = 1.92 years), (2) children who had a history of simple craniosynostosis not involving the metopic suture (M= 7.54 years, SD = 1.88 years), and (3) a group of children diagnosed with ADHD (M=7.78 year, SD = 1.87 years). It was found using the Home and School versions of the Attention Deficit Disorders Evaluation Scale (ADDES) that children with a history of metopic synostosis demonstrate significantly more behavioral disturbances than children with a history of simple craniosynostosis not involving the metopic suture. Using the BASC Teacher Rating Form it was found that children with a history of metopic synostosis have a behavior pattern similar to children diagnosed with ADHD and a dissimilar behavior pattern compared to children who have a history of craniosynostosis not involving the metopic suture. Using the BASC Parent Rating Form it was found that children with a history of metopic synostosis have a behavior pattern dissimilar to children diagnosed with ADHD and a dissimilar behavior pattern compared to children who have a history of craniosynostosis not involving the metopic suture.

Modeling human behavior on the Web is an essential goal of Web usage mining and is often performed by sequential pattern mining (SPM). The similarity between data elements and the variability of human behavior often result in the decrease of the support value of some patterns and thus limit the number of patterns mined. This is often associated with a loss of information. As the data can take the form of multiple data sources that represent multiple views of the area of interest, they can be used to handle this similarity and variability. Traditional approaches form a unique dataset from these data. However, the associated mining task is complex and generates a large and redundant set of sequential patterns. This work proposes G_SPM, a pattern mining algorithm designed for behavioral pattern mining that takes advantage of multi-source data to handle the similarity, while adopting a selective mining strategy to limit the complexity of the mining process and the increase in the number of patterns. It considers the behavioral data source as the main source, and exploits complementary sources only when similarity is suspected. It forms frequent general patterns that represent sets of similar behavioral patterns with a limited frequency, while controlling the level of generality. Experimental results confirm that G_SPM succeeds in mining general patterns and thus in handling the problem of item similarity. In addition G_SPM outperforms traditional approaches in terms of runtime and redundancy of the resulting set of patterns.

The possible effect of a specific overt behavior pattern upon serum cholesterol levels, blood coagulation, and incidence of arcus senilis and clinical coronary heart disease was studied in two groups of women randomly chosen for study by lay selectors after careful orientation. Women of group A were selected on the sole basis of their habitual exhibition of a behavior pattern (type A) characterized by excessive competitive "drive," ambitiousness, and immersion in "deadlines." Women of group B were selected by lay selectors on the sole basis of their exhibition of entirely opposite behavioral traits (type B). Each group of subjects included a series of nuns respectively exhibiting the type A and B behavior patterns. The women exhibiting pattern A also exhibited markedly higher serum cholesterol levels, often more rapid blood coagulation, three to five times as much arcus senilis, three to seven times more diastolic hypertension, and a four-fold higher incidence of clinical coronary disease. A history of myocardial infarction or angina pectoris only was found in group A. These striking differences were independent of differences in parental incidence of coronary disease, caloric or fat intake, obesity, physical activity, smoking, or "femininity." The strikingly higher incidence of clinical disease in pattern A women also was not found ascribable per se to differences in heredity, obesity, hypertension, caloric or fat intake, smoking, physical activity, blood coagulation, or serum cholesterol levels. The results closely resemble those previously found in men with similar behavior patterns, and strongly suggest that the type A behavior pattern is itself in some way largely responsible for the observed findings.

Recent years have witnessed an increased attention to studies of sex differences, partly because such differences offer important considerations for personalized medicine. While the presence of sex differences in human behaviour is well documented, our knowledge of their anatomical foundations in the brain is still relatively limited. As a natural gateway to fathom the human mind and behaviour, studies concentrating on the human brain network constitute an important segment of the research effort to investigate sex differences. Using a large sample of healthy young individuals, each assessed with diffusion MRI and a computerized neurocognitive battery, we conducted a comprehensive set of experiments examining sex-related differences in the meso-scale structures of the human connectome and elucidated how these differences may relate to sex differences at the level of behaviour. Our results suggest that behavioural sex differences, which indicate complementarity of males and females, are accompanied by related differences in brain structure across development. When using subnetworks that are defined over functional and behavioural domains, we observed increased structural connectivity related to the motor, sensory and executive function subnetworks in males. In females, subnetworks associated with social motivation, attention and memory tasks had higher connectivity. Males showed higher modularity compared to females, with females having higher inter-modular connectivity. Applying multivariate analysis, we showed an increasing separation between males and females in the course of development, not only in behavioural patterns but also in brain structure. We also showed that these behavioural and structural patterns correlate with each other, establishing a reliable link between brain and behaviour.

The article quantitatively investigates transformative changes in human behavioral patterns about nature due to using digital forests in relaxation practices. In the era of COVID-19, when physical outings and social interactions have been limited, there has been a growing trend for people to engage in digital interactions with nature. This article aims to quantify the impact of people’s behavior and interaction in the digital space on the change of personal behavioral patterns in the physical natural environment, as well as to analyze the potential effects of digital “bathing in the forest”. Since in work it is advisable to take into account the influence of hidden factors, structural equation modeling was used for the needs of this study. The primary data were the answers of 300 respondents, which were obtained based on the results of a survey of people in the capital region of Japan. A five-point Likert scale was used to evaluate and analyze respondents’ opinions. The internal reliability of the questionnaire scales was tested using Cronbach’s alpha. The work offers an integral indicator for assessing the personal characteristics of individuals, based on which two groups of respondents were formed – extroverts (the index is higher than the median) and introverts (the index is lower than the median). The study analyzed the pathways that lead to changes in human behavior caused by the development of digital natural relaxation methods. The work established a statistically significant relationship between the effect of digital “bathing in the forest” and a change in human behavior (correlation coefficient 0.872). According to the results of structural modeling, it was found that digital “forest bathing” has a significant mediating effect on the promotion of pro-environmental behavior (correlation coefficient is 0.634) and that users who regularly experience nature in cyberspace are more likely to change their behavior concerning nature. It is substantiated that people closer to introversion are more actively involved in the digital space and interact with others, and significantly more often change their behavior towards the environment. This study is a rare contribution demonstrating the potential of enjoying digital “forest bathing” to induce environmentally oriented behavior, even in the physical absence of forests.

An Agent-Based Approach to Aircraft Conflict Resolution with Constraints