Human Interaction Behavior Research Articles

Indoor mobility data encoding with TSTM-in: A topological-semantic trajectory model

The growing ubiquity of location/activity sensing technologies has created unprecedented opportunities for research on human spatiotemporal interaction behavior in mobile environments. However, existing studies of human mobility need to sufficiently account for the association of indoor scenes with the semantics of human behavior. This paper introduces TSTM-in, a trajectory model that combines trajectory data and indoor scenes using topological semantic modeling, semantic trajectory reconstruction, and trajectory queries. The model effectively manages indoor semantic trajectory data and extracts topological behavioral semantics by incorporating important points across a trajectory to reflect the semantics of key points connected to indoor corridors and regions. These topological semantics facilitate the creation of a flexible intersection-based indoor semantic trajectory reconstruction. Reconstructed semantic trajectories represent human mobility by integrating semantic data sets along the time axis. A case study with real-world trajectory queries from travelers demonstrates the model's effectiveness. TSTM-in realizes the association of indoor scenes with human behavior semantics, supporting the construction of mobile object management applications for indoor scenes and providing scientific and reasonable spatiotemporal semantic information description for location service-based intelligent cities.

The Behavioral Cost of Care: Changes in Maintenance Behavior during Equine-Assisted Interventions.

This study examined human-animal symbiosis in an animal-assisted intervention through observations of animal maintenance behaviors. The rise of psychotherapy, learning, and recreation incorporating animals warrants exploration of the welfare of the animals involved in these interventions. The analysis of welfare in multispecies engagements can be discussed in terms of symbiosis. Regarding an intervention's animal provider (e.g., therapy horse) and human recipient (psychotherapy client), the balance of cost and benefit is important. Research describing human and animal interactive behavior during interventions is limited, whether focusing on client outcomes or animal welfare. The present study adapted ethological methods to study humans and animals in an equine-assisted intervention, observing equine maintenance behaviors and equid-human interactive behavior. Maintenance behaviors were recorded before, during, and after equine-assisted (psychosocial) learning sessions with youth, providing 1600 observations. Equine alertness, eating behavior, and ambulation varied significantly before, during, and after the equine-assisted sessions. Such interruptions of typical behavior are an important aspect of welfare and unit of analysis when examining symbiotic relationships. A total of 267 sequences of equid-human approach-response behavior were also recorded, indicating that human-animal interaction was predominantly from humans toward equids. Equids' dominant response to human approach was no response, followed by avoidance, while humans' dominant response to equid approach was reciprocation. The findings are discussed in terms of symbiosis and animal welfare.

Integrating perspectives: contributions of Gestalt Theory to the understanding of Social Psychology

This article provides a comprehensive review of the significant contributions of Gestalt Theory to Social Psychology. It highlights how this perspective enriches the understanding of perception, cognition and social interaction. Originating in the field of perceptual psychology, Gestalt Theory postulates that individuals perceive visual and social elements as integrated parts of a whole, rather than sums of isolated components. This holistic principle is explored through its application in Social Psychology. How it influences impression formation, problem solving, decision making and group dynamics is examined. Key concepts such as insight thinking, figure background in social perception, and the importance of patterns and structures in interpreting social contexts are discussed. In addition, the impact of Gestalt Theory on research methods and social therapies is evaluated from a unique perspective on human interaction and group behavior. Finally, future research areas are suggested that could benefit from a deeper integration of Gestalt principles into Social Psychology, promoting a more holistic and dynamic approach to understanding the mind and social behavior.

Human-to-human interaction behaviors sensing based on complex-valued neural network using Wi-Fi channel state information

Next generation wireless communications will not only be able to transmit information faster but also can sense the physical world, i.e., integrated sensing and communications (ISAC). How to use or share part of the equipment to realize simultaneous communication and sensing is becoming a hot topic in both industry and academia. In this paper, we propose a human-to-human interaction behaviors sensing method based on the complex-valued neural network (CVNN), which can sense human-to-human interaction behaviors through the channel state information (CSI) of Wi-Fi signals. Specifically, according to the propagation characteristics of wireless signals in indoor scenarios and the Fresnel zone model, we analyze the changing relationship between the position or posture of the human body and the wireless signal. Then, by analyzing Wi-Fi signals captured in an indoor scenario, we explore the differences between different channels with the same human-to-human behavior and between different subcarriers of the same channel, and investigate the impacts of different human interaction behaviors on Wi-Fi signal propagation characteristics. In addition, to reduce the computation complexity and improve the sensing speed, we combine the existing channel models and remove the static signal components. Finally, we design a sensing model based on CVNN, and realize the sensing of human-to-human interaction behaviors through the CSI. The proposed method can sense 12 kinds of human-to-human interaction behaviors, and the overall sensing accuracy is 82%. This work contributes to the theoretical exploration and system design of Wi-Fi sensing.

Modelling communication-enabled traffic interactions.

A major challenge for autonomous vehicles is handling interactions with human-driven vehicles-for example, in highway merging. A better understanding and computational modelling of human interactive behaviour could help address this challenge. However, existing modelling approaches predominantly neglect communication between drivers and assume that one modelled driver in the interaction responds to the other, but does not actively influence their behaviour. Here, we argue that addressing these two limitations is crucial for the accurate modelling of interactions. We propose a new computational framework addressing these limitations. Similar to game-theoretic approaches, we model a joint interactive system rather than an isolated driver who only responds to their environment. Contrary to game theory, our framework explicitly incorporates communication between two drivers and bounded rationality in each driver's behaviours. We demonstrate our model's potential in a simplified merging scenario of two vehicles, illustrating that it generates plausible interactive behaviour (e.g. aggressive and conservative merging). Furthermore, human-like gap-keeping behaviour emerged in a car-following scenario directly from risk perception without the explicit implementation of time or distance gaps in the model's decision-making. These results suggest that our framework is a promising approach to interaction modelling that can support the development of interaction-aware autonomous vehicles.

Human object interaction detection based on feature optimization and key human-object enhancement

Aiming at the problem of unclear or missing human object interaction behavior objects in complex background, we propose a human object interaction detection algorithm based on feature optimization and key human-object enhancement. In order to solve the problem of missing human behavior objects, we propose Feature Optimized Faster Region Convolutional Neural Network (FOFR-CNN). FOFR-CNN is an object detection network optimized by multi-scale feature optimization algorithm, taking into account both image semantics and image structure. In order to reduce the interference of complex background, we propose a Key Human-Object Enhancement Network. The network uses an instance-based method to enhance the features of interactive objects. In order to enrich the interaction information, we use the graph convolutional network. Experimental results on HICO-DET, V-COCO and HOI-A datasets show that the proposed algorithm has significantly improved accuracy and multi-scale object detection ability compared with other human object interaction algorithms.

Towards Context-Aware Facial Emotion Reaction Database for Dyadic Interaction Settings.

Emotion recognition is a significant issue in many sectors that use human emotion reactions as communication for marketing, technological equipment, or human-robot interaction. The realistic facial behavior of social robots and artificial agents is still a challenge, limiting their emotional credibility in dyadic face-to-face situations with humans. One obstacle is the lack of appropriate training data on how humans typically interact in such settings. This article focused on collecting the facial behavior of 60 participants to create a new type of dyadic emotion reaction database. For this purpose, we propose a methodology that automatically captures the facial expressions of participants via webcam while they are engaged with other people (facial videos) in emotionally primed contexts. The data were then analyzed using three different Facial Expression Analysis (FEA) tools: iMotions, the Mini-Xception model, and the Py-Feat FEA toolkit. Although the emotion reactions were reported as genuine, the comparative analysis between the aforementioned models could not agree with a single emotion reaction prediction. Based on this result, a more-robust and -effective model for emotion reaction prediction is needed. The relevance of this work for human-computer interaction studies lies in its novel approach to developing adaptive behaviors for synthetic human-like beings (virtual or robotic), allowing them to simulate human facial interaction behavior in contextually varying dyadic situations with humans. This article should be useful for researchers using human emotion analysis while deciding on a suitable methodology to collect facial expression reactions in a dyadic setting.

Unveiling the Thematic Landscape of Cultural Studies Through Bibliometric Analysis

Introduction: the sociology of knowledge has been a source of interest for researchers. The implication of bibliometrics as a science for the construction of scientific knowledge in the different branches of knowledge, especially the social sciences, occupies an important center.Objective: characterize the research published in Scopus referring to cultural studies.Method: a bibliometric analysis was carried out on the articles referring to cultural studies published in the period 2018 – 2023 in journals subscribed to the Scopus database. The SciVal tool was used to collect the information. Bibliometric indicators were analyzed. Descriptive statistics were applied.Results: A total of 169434 documents were found in the thematic category Cultural Studies, of which 37.9% were open access, with a trend of increasing scientific production. In the period from 2018 to 2022, researchers worldwide have contributed 3674 topics on Cultural Studies. A marked concentration of these topics was observed in the area of Social Sciences and Psychology, which is to be expected if one takes into account that these are prominent areas due to their relevance in the study of human interaction and social behavior. The most productive countries were the United States, United Kingdom, Russia, Spain and Australia. On the other hand, with respect to collaboration indicators, 70.5% of the papers had no collaboration and only 6.9% of the studies had international collaboration, which is to be expected given that cultural studies are mostly developed in a local or regional context.Conclusions: scientific production in matters of cultural studies showed gradual growth in correspondence with topics of interest to the scientific community. The focus of the studies was located on high-impact journals in correlation with the behavior of the citations received. At the same time, the collaboration was characterized by its institutional nature.

Hiker on trail: Information practices in the wild. A mixed methods approach to investigate real-time mobile technology use by long-distance backpackers

Long distance hikers' information practices are considered via an ‘in-field’ methodology that generated rich experiential data. Participants documented and reflected on their experiences and use of mobile digital technology while undertaking a two-week coast-to-coast walk across the Scottish Highlands. Empirical evidence on the adoption and use of mobile digital technology by backpackers, and the implications this has for their practices and interactions is scarce. “Research in the Wild” (RITW) mixed methods facilitated exploration of how and why mobile digital technology is employed via analysis of in-field video-logs, interviews with eight participants, and a survey. Analysis of the research interview data provides insights into fieldwork participants' reactions to, and experiences with, the methods employed. The affordances and issues of the approach are discussed, highlighting the benefits and challenges of mixed methods for Human Computer Interaction and Information Behavior research in the arena of outdoor recreation.

Analysis of the optimal shooting angle in football matches based on network data mining

As an important research field in video understanding, human behavior recognition is widely used in intelligent monitoring, intelligent nursing, interactive behavior, and robot control. ObjectiveThe purpose of this study is to explore the analysis method of interaction behavior of soccer video. MethodsIn this study, we use scene simulation, big data analysis, experimental comparison, and other methods to analyze the interaction between players in a football video. The resultsof experiment and analysis of attack mode under interactive behavior show that the accuracy of attack direction determination results in traditional video analysis method and accurate analysis method is 75.60 % and 85.2 %, respectively. In this study, taking into account the two factors of staff proportion and average distance, the accuracy is improved to a certain extent, which can reach 91.5 %. It can be concluded that the deviation rate proposed in this study can better describe the position information of the football, can realize the detection of the basic attack mode, and can enable the computer to identify the attack direction of both players in the shortest time, which shows that the method is efficient and accurate. It provides a good analysis method for the research of human interaction behavior in football video and even in all kinds of videos.

An Urdu speech corpus for emotion recognition.

Emotion recognition from acoustic signals plays a vital role in the field of audio and speech processing. Speech interfaces offer humans an informal and comfortable means to communicate with machines. Emotion recognition from speech signals has a variety of applications in the area of human computer interaction (HCI) and human behavior analysis. In this work, we develop the first emotional speech database of the Urdu language. We also develop the system to classify five different emotions: sadness, happiness, neutral, disgust, and anger using different machine learning algorithms. The Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coefficient (LPC), energy, spectral flux, spectral centroid, spectral roll-off, and zero-crossing were used as speech descriptors. The classification tests were performed on the emotional speech corpus collected from 20 different subjects. To evaluate the quality of speech emotions, subjective listing tests were conducted. The recognition of correctly classified emotions in the complete Urdu emotional speech corpus was 66.5% with K-nearest neighbors. It was found that the disgust emotion has a lower recognition rate as compared to the other emotions. Removing the disgust emotion significantly improves the performance of the classifier to 76.5%.

Human interactive behaviour recognition method based on multi-feature fusion

Human interactive behaviour recognition method based on multi-feature fusion

Human interactive behaviour recognition method based on multi-feature fusion

Human interactive behaviour recognition method based on multi-feature fusion

Perancangan dan Evaluasi User Experience Aplikasi Virtual Event Menggunakan User Experience Questionnaire

Since the end of 2019, the world has experienced a COVID-19 pandemic which affects every aspect of life. To get around and adapt in the new normal era, technology is the main tool that plays a role in changing human interaction behavior from offline to online. This encourages the diffusion of digital technology in society. One of them is the organization of events such as seminars and workshops that must continue to take place from the needs of the community. The platform commonly used in conducting virtual events is Zoom Meeting. However, there are several features that have not been accommodated in Zoom Meeting. In this study, user experience and user interface design for the EventGo visual event application were designed. Design on user experience on Zoom Meeting. Therefore, an evaluation of the UX of the Zoom Meeting application was carried out using UEQ. The UI/UX design of the EventGo application also uses UEQ. From the evaluation results, it was found that there was an increase in the average UX aspect of the EventGo application design.

Assessment of Human Interaction and Spending Behavior Amidst Corona Virus in the United Arab Emirates

The COVID-19 pandemic has had unprecedented social and economic effects. In this study, the authors investigate the human interaction trends and spending behavior of individuals in the United Arab Emirates during the COVID-19 outbreak. An online survey was employed to assess the changes in human interactions and spending behavior among people in the United Arab Emirates. The findings indicate a decrease in spending on gasoline, restaurants, hotels, and coffee shops as well as an increase in spending on on-line shopping and groceries. During the pandemic, 80 percent of respondents reduced the number of visits with family and friends. Respondents also indicated greater cooperation among family members in performing household chores, with all members of the family, including children, participating in cooking, and increased cooperation between family members. The authors observed variations in the level and composition of consumer spending across the United Arab Emirates with variations by age, gender, and income level. The survey results highlight a specific relevance in government interventions tasked with the strategy, implementation, and monitoring of economic impacts arising from the spread of the coronavirus and forced public health measures to mitigate the health costs of the crisis. Supplemental data for this article is available online at https://doi.org/10.1080/23754931.2021.1950039

Human Interaction Behavior Recognition based on Local Spatial - Temporal and Global Feature

At present, the human interaction behavior algorithm has received great attention, but how to effectively extract the interactive characteristics of human interaction and establish the complex model between multiple targets is still a difficult problem. Aiming at this problem, we proposed a human interaction recognition method based on local spatial - temporal and global feature. This method treats people who are interacting as a whole. Firstly, foreground human object is extracted by the inter-frame difference. Secondly, the optical flow and space-time points of interest are extracted. Considering the large and high dimension of the feature, the characteristics of each frame image are described by the gradient direction histogram HOG and the optical flow histogram HOF. Then, BP neural network is used to classify each feature of each frame. Considering the limitations of different features for interactive behavior recognition, BP neural network combined with classification fusion is used to obtain the final interaction behavior category. The algorithm tested in the UT interaction dataset, and the experimental results show that this method can recognize the interaction behaviors of hitting, hugging, kicking, pushing and handshaking. The proposed method can be applied to intelligent video surveillance.

A Novel Haptic Feature Set for the Classification of Interactive Motor Behaviors in Collaborative Object Transfer.

Haptics provides a natural and intuitive channel of communication during the interaction of two humans in complex physical tasks, such as joint object transportation. However, despite the utmost importance of touch in physical interactions, the use of haptics is under-represented when developing intelligent systems. This ar̥ticle explores the prominence of haptic data to extract information about underlying interaction patterns within physical human-human interaction (pHHI). We work on a joint object transportation scenario involving two human partners, and show that haptic features, based on force/torque information, suffice to identify human interactive behavior patterns. We categorize the interaction into four discrete behavior classes. These classes describe whether the partners work in harmony or face conflicts while jointly transporting an object through translational or rotational movements. In an experimental study, we collect data from $\mathbf {12}$ human dyads and verify the salience of haptic features by achieving a correct classification rate over $\mathbf {91\%}$ using a Random Forest classifier.

Human interaction behavior modeling using Generative Adversarial Networks

Recently, considerable research has focused on personal assistant robots, and robots capable of rich human-like communication are expected. Among humans, non-verbal elements contribute to effective and dynamic communication. However, people use a wide range of diverse gestures, and a robot capable of expressing various human gestures has not been realized. In this study, we address human behavior modeling during interaction using a deep generative model. In the proposed method, to consider interaction motion, three factors, i.e., interaction intensity, time evolution, and time resolution, are embedded in the network structure. Subjective evaluation results suggest that the proposed method can generate high-quality human motions.

Elementary affordances: A study on physical user-product interactions

Digital human models enable a pro-active and virtual assessment of ergonomics in design. Despite this huge potential, possibilities of user-product interaction modelling, in terms of postures or movement generation, are limited and poorly integrated in common virtual design tools. To address this deficiency, we propose the concept of “elementary affordances”. These universally describe parts of human interaction behavior and shall serve as a modelling interface for posture and movement prediction algorithms. This paper presents an empirical study, identifying 18 elementary affordances, proving that parts of human interaction behavior can be described via a relatively small set of elementary affordances.

PENERAPAN STRATEGI E-MARKETING COMMUNICATION DAN EKUITAS MEREK SIARANKU.COM TERHADAP LOYALITAS VIEWERS

The development of communication technology has brought changes to the patterns of human interaction behavior which continue competing to innovate in developing and utilizing increasingly sophisticated communication technologies. In this study, researchers were using theories and concepts that support the basis of being integrated, such as Integrated Marketing Communication, Brands, Brand Equity, and Consumer Loyalty. Researchers were using a supportive method to be used as a methodology such as positivistic paradigm, quantitative approach, survey strategy using questionnaires, sampling techniques using the formula taro yamane, and data processing techniques using probability sampling - simple random sampling. The results showed that e-marketing communication and brand equity have a strong influence on the loyalty of siaranku.com viewers and have a validity level that meets the requirements.