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(1) Background: Social robot interaction design is crucial for determining user acceptance and experience. However, few studies have systematically discussed the current focus and future research directions of social robot interaction design from a bibliometric perspective. Therefore, we conducted this study in order to identify the latest research progress and evolution trajectory of research hotspots in social robot interaction design over the last decade. (2) Methods: We conducted a comprehensive review based on 2416 papers related to social robot interaction design obtained from the Web of Science (WOS) database. Our review utilized bibliometric techniques and integrated VOSviewer and CiteSpace to construct a knowledge map. (3) Conclusions: The current research hotspots of social robot interaction design mainly focus on #1 the study of human–robot relationships in social robots, #2 research on the emotional design of social robots, #3 research on social robots for children's psychotherapy, #4 research on companion robots for elderly rehabilitation, and #5 research on educational social robots. The reference co-citation analysis identifies the classic literature that forms the basis of the current research, which provides theoretical guidance and methods for the current research. Finally, we discuss several future research directions and challenges in this field. [ABSTRACT FROM AUTHOR]

Play involves diverse meaning-making for young children and grandparents. This paper derives from a larger intergenerational family practices project conducted in Australia during 2021–2022 against the backdrop of the COVID-19 pandemic. The research team undertook intergenerational group interviews and filmed family play activities in addition to collecting videoclips and photos of young children's play from six families. In this paper, vignettes from two families are described and given meaning. Rogoff's social learning three planes theory involving the intertwined and interdependent—'personal', 'interpersonal', and 'community'—planes is used to analyse the data. Our findings show the important role that grandparents play in facilitating children's learning including through the creation of linguistic and cultural heritage play spaces. We argue that intergenerational play practices can offer a means to integrate linguistic and cultural experiences in young children's lives by providing a space and time for social interaction and learning. [ABSTRACT FROM AUTHOR]

University systems are increasingly facing the consequences of the dark side of leadership. The dark side of leadership in educational organisations is a worldwide phenomenon affecting universities, academies, and college campuses. To respond to this, this paper explores the potential of community practice as an approach to respond to the dark side of leadership in the university system. The paper is a conceptual argument and derives knowledge from argumentative and logical reasoning, located within an interpretive worldview and analysed using conceptual analysis as a tool to make sense of the arguments. The paper argues that shared commonality, collective problem solving, mutual participation, and social interactions are dimensions of remedy that can ameliorate the dark side of leadership. Based on this argument, the paper concludes that effective dimensions of remedy that could ameliorate the dark side of leadership in university contexts are shared commonality, collective problem solving, mutual participation, and social interactions, with a recommendation that they must be incorporated in university leadership dispositions. [ABSTRACT FROM AUTHOR]

Human–computer interaction (HCI) research involves the design and use of computer technology, focusing in particular on the interfaces between people (users) and computers. HCI researchers observe the ways in which humans interact with computers and design technologies that allow them to interact in novel ways. As HCI evolves into HCI 2.0, user experiences and feedback become ever more relevant. This Special Issue, "Human Computer Interactions 2.0", presents 11 excellent papers about topics related to human–computer interactions. It aims to provide a broad international forum for world researchers, engineers and professionals in human–computer interaction research for the discussion and exchange of various scientific, technical and management discoveries across the world. [ABSTRACT FROM AUTHOR]

The objective of this systematic review is to investigate the various approaches that have been undertaken in finite element analysis (FEA) of human–seat interactions and synthesize the existing knowledge. With advances in numerical simulation and digital human modeling, FEA has emerged as a powerful tool to study seating comfort and discomfort. FEA employs various biomechanical factors to predict the contact stress and pressure distribution in a particular seat design. Given the complexity of human–seat interaction, several modeling and processing steps are required to conduct realistic FEA. The steps of how to perform an FEA simulation on human–seat interactions, the different models used, the model mesh compositions, and the material properties are discussed and reviewed in this paper. This can be used as a guideline for future studies in the context of FEA of human–seat interactions. [ABSTRACT FROM AUTHOR]

At the turn of the twentieth century, British women were able to qualify as medical doctors and enter professional practice for the first time. However, they often remained excluded from the specialist journals which were crucial for knowledge exchange during this period. As a result, they formed several of their own periodicals, including the Magazine of the London School of Medicine for Women (1895–1947), which this paper discusses. Significantly, the Magazine not only provided female doctors with the opportunity for intellectual communication, but social interaction too. This paper will explore how the periodical regularly published community-building content, which emphasised friendship as a key component of female doctors' relationships. The Magazine encouraged the sharing of humour, stories, and intimate news which both articulated and generated companionship amongst subscribers. Through this content, the Magazine wove professional connections into personal bonds, telling a story of medical sisterhood and offering a welcoming textual meeting place to a disparate network of female doctors. [ABSTRACT FROM AUTHOR]

Reducing cumbersome mechanical control components is one of the trends of modern agricultural machinery towards a high degree of automation. Whether the control components of sugarcane harvesters can gradually be replaced by central control screens, similar to car cabins, is an unresolved question. At the level of human machine interaction, this involves comparing the efficiency between joystick and touch screen control. This paper conducts a simulated experiment to compare and study the efficiency and user experience of joystick and touch screen control in adjusting the topper and base cutter heights of sugarcane harvesters, aiming to provide a reasonable basis for the design of control interfaces in sugarcane harvester cabins. The electromyographic signals, experiment duration, and subjective cognitive evaluations of participants in both the topper and base cutter groups were analyzed. The results showed that the efficiency, learnability, and ease of use of different control methods varied under different operational tasks. For the topper that corresponds to the operating behavior and height transformation in real time, joystick control demonstrated superior ease of use and operational efficiency compared to touch screen control, with weaker learnability. There was no discernible difference in muscle activation levels between the two control methods. Consequently, joystick control is deemed more suitable for the height adjustment of the topper. Regarding the base cutter with non-real-time mapping of operating behavior and height changes, no significant disparity in ease of use and learnability was observed between the two control methods. Touch screen control yielded lower muscle activation levels and garners higher overall subjective cognitive scale ratings. Thus, touch screen control is considered more suitable for base cutter height adjustment. Lastly, the paper proposes the optimal combination of software and hardware for control components in sugarcane harvester cabins, and provides an objective and multidimensional experimental analysis method for future research on similar human machine interaction interfaces. [ABSTRACT FROM AUTHOR]

This paper interprets the changing traits of religiosity in modern and postmodern societies from the perspective of spatial turn. The analysis examines the impact of social experience and action on spatial structure and how changes in spatial structure have influenced individual actions and experiences over the past decade, with a specific emphasis on the relationship to transcendence. The analysis explores the impact of the interaction of social spaces and actions on religiosity, in order to provide new insights into the interpretation of religious phenomena through a novel approach to the study of religion. It focuses on the consequences of individualisation, hybridisation, and globalisation, and analyses how these transformations are shaping contemporary religiosity in the global north. The paper argues that spatial structural changes are reinforcing more individualised forms of religiosity, often separated from traditional institutionalised religiosity. This gives greater scope to subject-organised 'patchwork religiosity', which inevitably reinforces a new kind of religious syncretism. The reflection unravels the spatial aspects of this transformation in a novel way. [ABSTRACT FROM AUTHOR]

Despite the increasing use of technologies in the classroom, there are concerns that technology-enhanced learning environments may hinder students' communication and interaction. In this study, we investigated how tangible technologies can enhance students' multimodal communication and interaction during equation-solving pair work compared to working without such technologies. A tangible app for learning equation solving was developed and tested in fourth- and fifth-grade classrooms with two class teachers and 24 students. Video data of the interventions were analysed using deductive and inductive content analysis. Coded data were also quantified for quantitative analysis. Additionally, teacher interview data were used to compare and contrast the findings. The findings showed that the tangible app better promoted students' multimodal communication and peer interaction than working only with paper and pencil. When working in pairs, tangible-app students interacted with one another much more often and in more ways than their paper-and-pencil peers. The implications of this study are discussed in terms of its contributions to research on tangible technologies for learning, educational technology development, and the use of tangibles in classrooms to support students' multimodal communication and peer interaction. [ABSTRACT FROM AUTHOR]

This paper proposes a method for multi-label visual emotion recognition that fuses fore-background features to address the following issues that visual-based multi-label emotion recognition often overlooks: the impacts of the background that the person is placed in and the foreground, such as social interactions between different individuals on emotion recognition; the simplification of multi-label recognition tasks into multiple binary classification tasks; and it ignores the global correlations between different emotion labels. First, a fore-background-aware emotion recognition model (FB-ER) is proposed, which is a three-branch multi-feature hybrid fusion network. It efficiently extracts body features by designing a core region unit (CR-Unit) that represents background features as background keywords and extracts depth map information to model social interactions between different individuals as foreground features. These three features are fused at both the feature and decision levels. Second, a multi-label emotion recognition classifier (ML-ERC) is proposed, which captures the relationship between different emotion labels by designing a label co-occurrence probability matrix and cosine similarity matrix, and uses graph convolutional networks to learn correlations between different emotion labels to generate a classifier that considers emotion correlations. Finally, the visual features are combined with the object classifier to enable the multi-label recognition of 26 different emotions. The proposed method was evaluated on the Emotic dataset, and the results show an improvement of 0.732% in the mAP and 0.007 in the Jaccard's coefficient compared with the state-of-the-art method. [ABSTRACT FROM AUTHOR]

We extend two KdVSKR models to fractional KdVSKR models with the Caputo derivative. The KdVSKR equation in (2+1)-dimension, which is a recent extension of the KdVSKR equation in (1+1)-dimension, can model the soliton resonances in shallow water. Applying the Hirota bilinear method, finite symmetry group method, and consistent Riccati expansion method, many new interaction solutions have been derived. Soliton and elliptical function interplaying solution for the fractional KdVSKR model in (1+1)-dimension has been derived for the first time. For the fractional KdVSKR model in (2+1)-dimension, two-wave interaction solutions and three-wave interaction solutions, including dark-soliton-sine interaction solution, bright-soliton-elliptic interaction solution, and lump-hyperbolic-sine interaction solution, have been derived. The effect of the order γ on the dynamical behaviors of the solutions has been illustrated by figures. The three-wave interaction solution has not been studied in the current references. The novelty of this paper is that the finite symmetry group method is adopted to construct interaction solutions of fractional nonlinear systems. This research idea can be applied to other fractional differential equations. [ABSTRACT FROM AUTHOR]

Road traffic accidents result in numerous fatalities and injuries annually. Advanced driving assistance systems (ADASs) have garnered significant attention to mitigate these harms. An accurate safety assessment can significantly improve the effectiveness and credibility of ADASs. However, a real-time safety assessment remains a key challenge due to the complex interactions among humans, vehicles, and the road environment. Traditional safety assessment methods, relying on crash data and surrogate safety measures (SSMs), face limitations in real-time applicability and scenario coverage, especially in freeway ramp areas with frequent merging and lane changing. To address these gaps, this paper develops a driving safety field based on the uncertainty of vehicle movements, which integrates the characteristics of driving behaviors, vehicles, and the road environment. The proposed method is validated with a simulation of driving scenarios and ROC curves obtained from the NGSIM dataset. The results demonstrate that our proposed driving safety field effectively quantifies the real-time risk in ramp influence areas and outperforms Time to Collision (TTC), making it suitable for integration into collision warning systems of ADASs. [ABSTRACT FROM AUTHOR]

This study reviewed previous studies on place attachment and related factors in the urban setting. Assessments were conducted on over one hundred peer-reviewed journal papers that met the selection criteria. The results were divided into six categories. Place attachment can improve each of the three dimensions of wellbeing. It can also mitigate the negative impacts of environmental stressors on wellbeing. Furthermore, traditional gardens and local landscape characteristics contribute to a stronger place attachment, depending on the degree to which they present local history and culture. Green landscapes can also help immigrants feel more connected to their community and place of origin. Social interaction has also been linked to the development of place attachment, which is stronger in low-income/deprived neighborhoods. Furthermore, higher place attachment promotes civic engagement and social trust. Place attachment also helps older people cope with aging difficulties in their living environments. Finally, there is a mutually beneficial association between place attachment and perceived neighborhood walkability, as well as a relationship between the scale of the place and the level of attachment. The various research gaps recognized by this study could be addressed in future studies to better understand the role of place attachment in creating sustainable urban environments. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive approach to detect violent events in videos by combining CrimeNet, a Vision Transformer (ViT) model with structured neural learning and adversarial regularization, with an adaptive threshold sliding window model based on the Transformer architecture. CrimeNet demonstrates exceptional performance on all datasets (XD-Violence, UCF-Crime, NTU-CCTV Fights, UBI-Fights, Real Life Violence Situations, MediEval, RWF-2000, Hockey Fights, Violent Flows, Surveillance Camera Fights, and Movies Fight), achieving high AUC ROC and AUC PR values (up to 99% and 100%, respectively). However, the generalization of CrimeNet to cross-dataset experiments posed some problems, resulting in a 20–30% decrease in performance, for instance, training in UCF-Crime and testing in XD-Violence resulted in 70.20% in AUC ROC. The sliding window model with adaptive thresholding effectively solves these problems by automatically adjusting the violence detection threshold, resulting in a substantial improvement in detection accuracy. By applying the sliding window model as post-processing to CrimeNet results, we were able to improve detection accuracy by 10% to 15% in cross-dataset experiments. Future lines of research include improving generalization, addressing data imbalance, exploring multimodal representations, testing in real-world applications, and extending the approach to complex human interactions. [ABSTRACT FROM AUTHOR]

The rapid development of the Internet has facilitated expression, sharing, and interaction on social networks, but some speech may contain harmful discrimination. Therefore, it is crucial to classify such speech. In this paper, we collected discriminatory data from Sina Weibo and propose the improved Synthetic Minority Over-sampling Technique (SMOTE) algorithm based on Latent Dirichlet Allocation (LDA) to improve data quality and balance. And we propose a new integration method integrating Support Vector Machine (SVM) and Random Forest (RF). The experimental results demonstrate that the integrated model exhibits enhanced precision, recall, and F1 score by 6.0%, 5.4%, and 5.7%, respectively, in comparison with SVM alone. Moreover, it exhibits the best performance in comparison with other machine learning methods. Furthermore, the positive impact of improved SMOTE and this integrated method on model classification is also confirmed in ablation experiments. [ABSTRACT FROM AUTHOR]

Social interactions are characterized by being very diverse and changing over time. Understanding this diversity and dynamics, as well as their emerging patterns, is of great interest from social, health, and educational perspectives. The development of new devices has been made possible in recent years by advances in applied technology. This paper presents the design and development of a novel device composed of several sensors. Specifically, we propose a proximity sensor integrated by three devices: a Bluetooth sensor, a global positioning system (GPS) unit and an accelerometer. By means of this sensor it is possible to detect the presence of neighboring sensors in various configurations and operating conditions. Profiles based on the Received Signal Strength Indicator (RSSI) exhibit behavior consistent with that reported by empirical relationships. The present sensor is functional in detecting the proximity of other sensors and is thus useful for the identification of interactions between people in relevant contexts such as schools. [ABSTRACT FROM AUTHOR]

A critical step in the visual navigation of unmanned surface vehicles (USVs) is horizon line detection, which can be used to adjust the altitude as well as for obstacle avoidance in complex environments. In this paper, a real-time and accurate detection method for the horizon line is proposed. Our approach first differentiates the complexity of navigational scenes using the angular second moment (ASM) parameters in the grey level co-occurrence matrix (GLCM). Then, the region of interest (ROI) is initially extracted using minimal human interaction for the complex navigation scenes, while subsequent frames are dynamically acquired using automatic feature point matching. The matched ROI can be maximally removed from the complex background, and the Zernike-moment-based edges are extracted from the obtained ROI. Finally, complete sea horizon information is obtained through a linear fitting of the lower edge points to the edge information. Through various experiments carried out on a classical dataset, our own datasets, and that of another previously published paper, we illustrate the significance and accuracy of this technique for various complex environments. The results show that the performance has potential applications for the autonomous navigation and control of USVs. [ABSTRACT FROM AUTHOR]

Simple Summary: Horses are highly social animals that preferably live in stable social groups and form long-term affiliative bonds. However, although their need for social interaction has not changed with domestication, domestic horses are often housed in individual stables with limited social contact with other horses or in group housing with regular changes in their group composition. Thus, this review aims to provide an overview of social ethograms to facilitate the inclusion of social behaviour in equine welfare assessment. A literature review yielded 27 papers that studied equine adult social behaviour using a well-defined ethogram. Social interactions were observed in 851 horses living in groups of 9.1 (mean +/− 6.8 s.d., range: 2–33) horses. A total of 40 (mean: 12.8/paper, range: 2–23) social behaviours were described, of which 60% (24/40) were agonistic, 30% (12/40) affiliative, 7.5% (3/40) investigative and 2.5% (1/40) neutral. The 27 papers focused predominantly on socio-negative interactions by including 67.7% agonistic and only 26% affiliative, 5.1% investigative and 1.2% neutral social behaviours in their research. The strong emphasis on agonistic behaviour contrasts sharply with the rarity of agonistic behaviour in stable horse groups and the well-established importance of affiliative interactions for equine welfare. Therefore, to advance the assessment of horses' welfare, the ethogram needs to be refined to reflect the nuanced and complex equine social behaviour better and consider more affiliative and also ambivalent and socially tolerant interactions. Sociality is an ethological need of horses that remained unchanged by domestication. Accordingly, it is essential to include horses' social behavioural requirements and the opportunity to establish stable affiliative bonds in equine management systems and welfare assessment. Thus, this systematic review aims to provide an up-to-date analysis of equine intraspecific social ethograms. A literature review yielded 27 papers that met the inclusion criteria by studying adult (≥2 years) equine social behaviour with conspecifics using a well-defined ethogram. Social interactions were observed in 851 horses: 320 (semi-)feral free-ranging, 62 enclosed (semi-)feral and 469 domesticated, living in groups averaging 9.1 (mean +/− 6.8 s.d., range: 2–33) horses. The ethograms detailed in these 27 studies included a total of 40 (mean: 12.8/paper, range: 2–23) social behaviours, of which 60% (24/40) were agonistic, 30% (12/40) affiliative, 7.5% (3/40) investigative and 2.5% (1/40) neutral. The 27 publications included 67.7% agonistic and only 26% affiliative, 5.1% investigative and 1.2% neutral social behaviours in their methodology, thus focusing predominantly on socio-negative interactions. The strong emphasis on agonistic behaviours in equine ethology starkly contrasts with the rare occurrence of agonistic behaviours in stable horse groups and the well-established importance of affiliative interactions for equine welfare. The nuanced and complex equine social behaviour requires refinement of the ethogram with a greater focus on affiliative, ambivalent and indifferent interactions and the role of social tolerance in equine social networks to advance equine welfare assessment. [ABSTRACT FROM AUTHOR]

A large amount of research is dedicated to the measurement of immigration trends and integration processes, but comparative studies of the components of immigrants' well-being are rare. This paper investigates the link between the length of stay and the subjective well-being (SWB) of immigrants. A step-by-step regression method is used to understand interactions between different individual- and macro-level factors in the life satisfaction of immigrants. The results of the European Social Survey (ESS) show that the effect of length of stay on SWB is mitigated by numerous individual- and country-level variables. After all background variables are considered, newly arrived immigrants and those who have been in the new country for more than 20 years had a similar life satisfaction to that of the local-born population. Immigrants with 10 to 20 years' experience in the country seem to have lower life satisfaction than the local-born population. The SWB of different immigrant groups is shaped by cultural background, economic coping, number of social contacts, perception of discrimination, and democracy. These factors influence the life satisfaction of migrants with different lengths of stay differently. The most vulnerable immigrant groups in Europe are those from Africa region. [ABSTRACT FROM AUTHOR]

Offline handwritten text recognition (HTR) is a long-standing research project for a wide range of applications, including assisting visually impaired users, humans and robot interactions, and the automatic entry of business documents. However, due to variations in writing styles, visual similarities between different characters, overlap between characters, and source document noise, designing an accurate and flexible HTR system is challenging. The problem becomes serious when the algorithm has a low learning capacity and when the text used is complex and has a lot of characters in the writing system, such as Ethiopic script. In this paper, we propose a new model that recognizes offline handwritten Ethiopic text using a gated convolution and stacked self-attention encoder–decoder network. The proposed model has a feature extraction layer, an encoder layer, and a decoder layer. The feature extraction layer extracts high-dimensional invariant feature maps from the input handwritten image. Using the extracted feature maps, the encoder and decoder layers transcribe the corresponding text. For the training and testing of the proposed model, we prepare an offline handwritten Ethiopic text-line dataset (HETD) with 2800 samples and a handwritten Ethiopic word dataset (HEWD) with 10,540 samples obtained from 250 volunteers. The experiment results of the proposed model on HETD show a 9.17 and 13.11 Character Error Rate (CER) and Word Error Rate (WER), respectively. However, the model on HEWD shows an 8.22 and 9.17 CER and WER, respectively. These results and the prepared datasets will be used as a baseline for future research. [ABSTRACT FROM AUTHOR]

Presently, a substantial majority of older individuals in urban regions of China prefer to inhabit older residential communities over newer counterparts. Within these aging communities, the intricate matter of intergenerational communication among older adults presents a complex and multifaceted issue that warrants comprehensive investigation from a systematic perspective. This paper first employs the observational method to study multiple old residential communities in a city in the Yangtze River Delta region of China. The POEMS framework and the AEIOU framework are applied, focusing on the analysis of individuals and the interaction between individuals and objects, respectively. Semistructured interviews are then conducted with three groups of people, emphasizing community participation by older adults, intergenerational interaction from the perspective of older adults, and intergenerational interaction from the perspective of young people. Finally, the paper categorizes the types and characteristics of individuals in the old communities, identifying the intersections between these groups. The current social situation of older adults and young people is summarized, including behavioral and psychological characteristics and social interaction challenges. Based on these findings, ten system design directions to enhance intergenerational interaction in old communities are proposed, and three of these system design directions are further developed. [ABSTRACT FROM AUTHOR]

Background and aim of the work: The COVID-19 pandemic has deeply affected the quality of people's social life, strongly impacting family dynamics, too, not only in the harshest periods of the pandemic but also afterwards. Pandemic-related measures led to a 'stay-at-home' approach that increased the mental and physical burdens of family caregivers, irrespective of whether they were living together with the person they were caring for or not. In this paper, we provide an overview of the impact that the COVID-19 pandemic had on family relationships and dynamics, as well as on family caregivers' mental burden, and outline how this developed. Methods: We collected relevant info by searching the PubMed/Medline database with appropriate keywords. The search was performed up to 28 February 2023. This paper is reported in line with PRISMA guidance. Results: Given the recent onset of the issue, the number of relevant papers was limited. However, the additional burden that the pandemic has caused worldwide to informal caregivers clearly emerges. Conclusions: The worldwide impact of the pandemic on informal caregiving is assessed, and recommendations on how the issue can be handled are briefly sketched, too. [ABSTRACT FROM AUTHOR]

This paper presents a collaborative manufacturing cell implemented in a laboratory setting, focusing on developing learning-based interaction abilities to enhance versatility and ease of use. The key components of the system include 3D real-time volumetric monitoring for safety, visual recognition of hand gestures for human-to-robot communication, classification of physical-contact-based interaction primitives during handover operations, and detection of hand–object interactions to anticipate human intentions. Due to the nature and complexity of perception, deep-learning-based techniques were used to enhance robustness and adaptability. The main components are integrated in a system containing multiple functionalities, coordinated through a dedicated state machine. This ensures appropriate actions and reactions based on events, enabling the execution of specific modules to complete a given multi-step task. An ROS-based architecture supports the software infrastructure among sensor interfacing, data processing, and robot and gripper controllers nodes. The result is demonstrated by a functional use case that involves multiple tasks and behaviors, paving the way for the deployment of more advanced collaborative cells in manufacturing contexts. [ABSTRACT FROM AUTHOR]

Collaborative robots are used in scenarios requiring interaction with humans. In order to improve the safety and adaptability of collaborative robots during human–robot interaction, this paper proposes a modular wire-actuated robotic arm with symmetric variable-stiffness units. The variable-stiffness unit is employed to extend the stiffness-adjustment range of the robotic arm. The variable-stiffness unit is designed based on flexure, featuring a compact and simple structure. The stiffness–force relationship of the variable-stiffness unit can be fitted by a quadratic function with an R-squared value of 0.99981, indicating weak nonlinearity. Based on the kinematics and stiffness analysis of the symmetric joint module of the robotic arm, the orientation of the joint module can be adjusted by regulating the length of the wires and the stiffness of the joint module can be adjusted by regulating the tension of the wires. Because of the actuation redundancy, the orientation and stiffness of the joint module can be adjusted synchronously. Furthermore, a direct method is proposed for the stiffness-oriented wire-tension-distribution problem of the 1-DOF joint module. A simulation is carried out to verify the proposed method. The simulation result shows that the deviation between the calculated stiffness and the desired stiffness was less than 0.005%. [ABSTRACT FROM AUTHOR]

Loopholes involve misalignments between rules about what should be done and what is actually done in practice. The focus of this paper is loopholes in interactions between human organizations' implementations of task-specific artificial intelligence and individual people. The importance of identifying and addressing loopholes is recognized in safety science and in applications of AI. Here, an examination is provided of loophole sources in interactions between human organizations and individual people. Then, it is explained how the introduction of task-specific AI applications can introduce new sources of loopholes. Next, an analytical framework, which is well-established in safety science, is applied to analyses of loopholes in interactions between human organizations, artificial intelligence, and individual people. The example used in the analysis is human–artificial intelligence systems in gig economy delivery driving work. [ABSTRACT FROM AUTHOR]

In order to solve the problems of traditional mental stress detection in college students that are time-consuming, random, and subjective, this paper proposes an intelligent perception-driven mental stress assessment method for college students. First, we analyze the factors in SRQ and SCL-90, which can be measured by intelligent sensing methods, including sleep, exercise, social interaction, and environment, and then perform feature extraction. Secondly, we use machine learning methods to build a mental stress assessment model. The Shapley additive explanations (SHAP) model is used to explain the training results. Experimental results show that the model proposed in this article can effectively assess the mental stress state of college students. This means that the collection of intelligent perception data based on the mental stress scale can effectively evaluate the mental stress state of college students and provide a new research idea for further developing a non-intrusive and real-time mental stress assessment for college students. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) is a fairly new technology enabling human machine interaction by superimposing virtual information on a real environment. Potential applications can be found in many areas of research from recent years. This study presents a systematic review of existing AR systems in the automotive field, synthesizing 55 studies from 2002 to 2019. The main research questions are: where AR technology has been applied within the automotive industry, what is the purpose of its application, what are the general characteristics of these systems, and what are the emphasized benefits and challenges of using AR in this field? The aim of this paper is to provide an insight into the AR applications and technologies in the automotive field. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue titled "Social and Technological Interactions in e-Societies." The issue explores the interactions between new technologies and society, addressing both the potential benefits and concerns they bring. The papers cover a range of topics, including the impact of artificial intelligence on higher education and leadership, the role of digitalization in government competitiveness, the use of digital systems during the COVID-19 pandemic, and the tools of digitalization for sustainability. The articles provide empirical evidence and examples of these interactions, highlighting the complexity and unpredictability of incorporating new technologies into society. [Extracted from the article]

Assistive devices like exoskeletons undergo extensive testing not least because of their close interaction with humans. Conducting user studies is a time-consuming process that demands expert knowledge, and it is accompanied by challenges such as low repeatability and a potential lack of comparability between studies. Obtaining objective feedback on the exoskeleton's performance is crucial for developers and manufacturers to iteratively improve the design and development process. This paper contributes to the concept of using robots for objective exoskeleton testing by presenting various approaches to a robotic-based testing platform for upper-body exoskeletons. We outline the necessary requirements for realistically simulating use cases and evaluate different approaches using standard manipulators as robotic motion generators. Three approaches are investigated: (i) Exploiting the anthropomorphic structure of the robotic arm and directly placing it into the exoskeleton. (ii) Utilizing a customized, direct attachment between the robot and exoskeleton. (iii) Attaching a human arm dummy to the robot end effector to simulate a more realistic interface with the exoskeleton. Subsequently, we discuss and compare the results against the aforementioned requirements of a systematic testing platform. Our conclusion emphasizes that achieving objective and realistic testing necessitates highly specialized hardware, algorithms, and further research to address challenging requirements. [ABSTRACT FROM AUTHOR]

Behavioral recognition is an important technique for recognizing actions by analyzing human behavior. It is used in various fields, such as anomaly detection and health estimation. For this purpose, deep learning models are used to recognize and classify the features and patterns of each behavior. However, video-based behavior recognition models require a lot of computational power as they are trained using large datasets. Therefore, there is a need for a lightweight learning framework that can efficiently recognize various behaviors. In this paper, we propose a group-based lightweight human behavior recognition framework (GLBRF) that achieves both low computational burden and high accuracy in video-based behavior recognition. The GLBRF system utilizes a relatively small dataset to reduce computational cost using a 2D CNN model and improves behavior recognition accuracy by applying location-based grouping to recognize interaction behaviors between people. This enables efficient recognition of multiple behaviors in various services. With grouping, the accuracy was as high as 98%, while without grouping, the accuracy was relatively low at 68%. [ABSTRACT FROM AUTHOR]

Simple Summary: The mugger is facing complex threats in today's world. This review aims to assess and characterize the existing world population of muggers in reference to its historical range, as well as perform habitat suitability assessment and threat assessment for each site. Based on extensive literature, we found that the chance of preserving muggers varies greatly among countries and habitats across its range. While two main groups of threats can be defined—habitat degradation and human–crocodile conflict—each site displays different characteristics and severity of these threats. We believe this work can be used as a basis to understand the current state and challenges faced by muggers today. The mugger (Crocodylus palustris) is a medium-sized crocodilian inhabiting South Asia. As a result of intensive hunting, its range declined drastically up till the 1970s. Currently, the world mugger population is fragmented and threatened mainly by habitat loss and the consequences of human–crocodile conflict, being classified as Vulnerable by the IUCN. The goal of this paper is to comprehensively determine the mugger's current range, and assess risks in notable habitats of the species across its range. To determine the range and notable habitats, extensive literature covering surveys, monitoring, population studies and reports of human–crocodile conflict was examined. Habitat suitability and risk assessment were performed by evaluating selected habitats using eight factors: the legal status of the area, elevation, surface water availability, water quality, salinity, availability of nesting and basking sites, interaction with humans and interspecific competition. Based on our findings, the chances of the mugger's survival varies greatly across its range and the threats they face are complex and often site-specific. Defining these threats is the first step for determining suitable risk mitigation efforts, some of which are explored in this review. [ABSTRACT FROM AUTHOR]

In the field of education, investigating the relationships between different majors in universities is an important topic in current educational research. The application of social networks from informatics provides new opportunities and potentials for the field of education. Due to the complexity of social interactions, the social network connections surrounding individuals exert a significant influence on their daily decision-making processes. This paper aims to introduce the social network and influence analysis theories from informatics into the field of education, regarding major as a variable, and comparing and analyzing the influence relationships between majors. An empirical study was conducted, involving the collection of questionnaire data on graduates' evaluations of various aspects of their university experiences across different majors. The evolution of this model follows the DeGroot opinion dynamics with the inclusion of stubborn nodes. By defining leader majors and general majors based on the data and modeling the questionnaire data as the outcome of a discrete random process, an influence matrix is ultimately generated through the opinion dynamic model. Through this modeling approach, we revealed the underlying influence relationships between different disciplines (majors). These findings provide schools with insights to adjust the directions of discipline cultivation, and offer new perspectives and methods for the study of majors in higher education. [ABSTRACT FROM AUTHOR]

This study conducts an in-depth review of the Social Internet of Things (SIoT), a significant advancement from the conventional Internet of Things (IoT) via the integration of socialization principles akin to human interactions. We explore the architecture, trust management, relationship dynamics, and other crucial aspects of SIoT, with a particular focus on the relatively neglected areas of fault tolerance, cloud–fog computing, and clustering. Our systematic literature analysis, spanning research from 2011 to April 2023, uncovers critical gaps and establishes a detailed taxonomy of emerging SIoT themes. This paper not only sheds light on the current state of SIoT research but also charts a course for future exploration and development in this burgeoning field. [ABSTRACT FROM AUTHOR]

Simulation models are a valuable tool for exoskeleton development, especially for system optimization and evaluation. It allows an assessment of the performance and effectiveness of exoskeletons even at an early stage of their development without physical realization. Due to the closed physical interaction between the exoskeleton and the user, accurate modeling of the human–exoskeleton interaction in defined scenarios is essential for exoskeleton simulations. This paper presents a novel approach to simulate exoskeleton motion in response to human motion and the interaction forces at the physical interfaces between the human and the exoskeleton. Our approach uses a multibody model of a shoulder exoskeleton in MATLAB R2021b and imports human motion via virtual markers from a digital human model to simulate human–exoskeleton interaction. To validate the human-motion-based approach, simulated exoskeleton motion and interaction forces are compared with experimental data from a previous lab study. The results demonstrate the feasibility of our approach to simulate human–exoskeleton interaction based on human motion. In addition, the approach is used to optimize the support profile of an exoskeleton, indicating its potential to assist exoskeleton development prior to physical prototyping. [ABSTRACT FROM AUTHOR]

The advent of Industry 4.0 necessitates substantial interaction between humans and machines, presenting new challenges when it comes to evaluating the stress levels of workers who operate in increasingly intricate work environments. Undoubtedly, work-related stress exerts a significant influence on individuals' overall stress levels, leading to enduring health issues and adverse impacts on their quality of life. Although psychological questionnaires have traditionally been employed to assess stress, they lack the capability to monitor stress levels in real-time or on an ongoing basis, thus making it arduous to identify the causes and demanding aspects of work. To surmount this limitation, an effective solution lies in the analysis of physiological signals that can be continuously measured through wearable or ambient sensors. Previous studies in this field have mainly focused on stress assessment through intrusive wearable systems susceptible to noise and artifacts that degrade performance. One of our recently published papers presented a wearable and ambient hardware-software platform that is minimally intrusive, able to detect human stress without hindering normal work activities, and slightly susceptible to artifacts due to movements. A limitation of this system is its not very high performance in terms of the accuracy of detecting multiple stress levels; therefore, in this work, the focus was on improving the software performance of the platform, using a deep learning approach. To this purpose, three neural networks were implemented, and the best performance was achieved by the 1D-convolutional neural network with an accuracy of 95.38% for the identification of two levels of stress, which is a significant improvement over those obtained previously. [ABSTRACT FROM AUTHOR]

The emergence of the Metaverse is raising important questions in the field of human–machine interaction that must be addressed for a successful implementation of the new paradigm. Therefore, the exploration and integration of both technology and human interaction within this new framework are needed. This paper describes an innovative and technically viable proposal for virtual shopping in the fashion field. Virtual hands directly scanned from the real world have been integrated, after a retopology process, in a virtual environment created for the Metaverse, and have been integrated with digital nails. Human interaction with the Metaverse has been carried out through the acquisition of the real posture of the user's hands using an infrared-based sensor and mapping it in its virtualized version, achieving natural identification. The technique has been successfully tested in an immersive shopping experience with the Meta Quest 2 headset as a pilot project, where a transactions mechanism based on the blockchain technology (non-fungible tokens, NFTs) has allowed for the development of a feasible solution for massive audiences. The consumers' reactions were extremely positive, with a total of 250 in-person participants and 120 remote accesses to the Metaverse. Very interesting technical guidelines are raised in this project, the resolution of which may be useful for future implementations. [ABSTRACT FROM AUTHOR]

In this paper, a new approach involving the use of a mobile manipulator to assist humans with mobility impairments to walk is proposed. First, in order to achieve flexible interaction between humans and mobile manipulators, we propose a variable admittance controller that can adaptively regulate the virtual mass and damping parameters based on the interaction forces and the human motion intention predicted using the fuzzy theory. Moreover, a feedforward velocity compensator based on a designed state observer is proposed to decrease the inertia resistance of the manipulator, effectively enhancing the compliance of the human–robot interaction. Then, the configuration of the mobile manipulator is optimized based on a null-space approach by considering the singularity, force capacity, and deformation induced by gravity. Finally, the proposed assisted walking approach for the mobile manipulator is implemented using the human–robot interaction controller and the null-space controller. The validity of the proposed controllers and the feasibility of assisted human walking are verified by conducting a set of tests involving different human volunteers. [ABSTRACT FROM AUTHOR]

In recent years, educational researchers and practitioners have become increasingly interested in new technologies for teaching and learning, including augmented reality (AR). The literature has already highlighted the benefit of AR in enhancing learners' outcomes in natural sciences, with a limited number of studies exploring the support of AR in social sciences. Specifically, there have been a number of systematic and scoping reviews in the AR field, but no peer-reviewed review studies on the contribution of AR within interventions aimed at teaching or training behavioral skills have been published to date. In addition, most AR research focuses on technological or development issues. However, limited studies have explored how technology affects social experiences and, in particular, the impact of using AR on social behavior. To address these research gaps, a scoping review was conducted to identify and analyze studies on the use of AR within interventions to teach behavioral skills. These studies were conducted across several intervention settings. In addition to this research question, the review reports an investigation of the literature regarding the impact of AR technology on social behavior. The state of the art of AR solutions designed for interventions in behavioral teaching and learning is presented, with an emphasis on educational and clinical settings. Moreover, some relevant dimensions of the impact of AR on social behavior are discussed in more detail. Limitations of the reviewed AR solutions and implications for future research and development efforts are finally discussed. [ABSTRACT FROM AUTHOR]

Featured Application: In this paper, a Multi-Attribute Non-Maximum Suppression (MA-NMS) algorithm, which adaptively adjusts suppression based on density and count attributes, is proposed. It can help detectors to obtain more accurate predictions in crowded scenes, which will benefit subsequent tasks regarding pedestrian detection, like face recognition, pedestrian re-identification, and human interaction. The proposed MA-NMS and the attribute branch (ATTB) can be easily embedded into generic pedestrian detectors for performance improvement. Moreover, with the proposed ATTB, a pedestrian detector is proposed, based on the MA-NMS, which can be directly used for pedestrian detection in crowded scenes, such as shopping malls, streets, airports, etc. Removing duplicate proposals is a critical process in pedestrian detection, and is usually performed via Non-Maximum Suppression (NMS); however, in crowded scenes, the detection proposals of occluded pedestrians are hard to distinguish from duplicate proposals, making the detection results inaccurate. In order to address the above-mentioned problem, the authors of this paper propose a Multi-Attribute NMS (MA-NMS) algorithm, which combines density and count attributes in order to adaptively adjust suppression, effectively preserving the proposals of occluded pedestrians while removing duplicate proposals. In order to obtain the density and count attributes, an attribute branch (ATTB), which uses a context extraction module (CEM) to extract the context of pedestrians, and then, concatenates the context with the features of pedestrians in order to predict both the density and count attributes simultaneously, is also proposed. With the proposed ATTB, a pedestrian detector, based on MA-NMS, is constructed for pedestrian detection in crowded scenes. Extensive experiments are conducted using the CrowdHuman and CityPersons datasets, and the results show that the proposed method outperforms mainstream methods on A P (average precision), R e c a l l , and M R − 2 (log-average miss rate), sufficiently validating the effectiveness of the proposed MA-NMS algorithm. [ABSTRACT FROM AUTHOR]

We were promised that the Metaverse would revolutionize our lives, social interactions, work, and business. However, how and when will this happen? We have seen the growth and development of technology, but there is no agreement or prediction about a specific time, and we can only follow the how question. To investigate more leads about this concept, we considered a main research question: How is the Metaverse actually being perceived? This question is connected with three objectives: to verify how the Metaverse is being represented and characterized, identify the main dimensions that facilitate or influence the acceptance of the Metaverse, and identify the leading technologies that suit the Metaverse concept. This study consisted of a documental analysis—or meta-analysis—of fifty of the most relevant scientific papers (taking into account some inclusion criteria) published in the last three years, using the Leximancer software to create concept maps to illustrate the main concepts and themes extracted from the articles to understand their associations or relations with the Metaverse concept. This study provided us with essential findings about how this concept has been perceived and allowed us to answer our objectives, contributing to a scientific discussion on the topic, and provided some valid suggestions for future research, which is already in progress. It also provided new leads on approaching this concept in development. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) applications have recently emerged for entertainment and educational purposes and have been proposed to have positive effects on social interaction. In this study, we investigated the impact of a mobile, indoor AR feature on sociability, entertainment, and learning. We conducted a field experiment using a quiz game in a Finnish science center exhibition. We divided participants (N = 372) into an experimental group (AR app users) and two control groups (non-AR app users; pen-and-paper participants), including 28 AR users of follow-up interviews. We used Kruskal–Wallis rank test to compare the experimental groups and the content analysis method to explore AR users' experiences. Although interviewed AR participants recognized the entertainment value and learning opportunities for AR, we did not detect an increase in perceived sociability, social behavior, positive affect, or learning performance when comparing the experimental groups. Instead, AR interviewees experienced a strong conflict between the two different realities. Despite the engaging novelty value of new technology, performance and other improvements do not automatically emerge. We also discuss potential conditional factors. Future research and development of AR and related technologies should note the possible negative effects of dividing attention to both realities. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has brought to the forefront the importance of predicting and controlling an epidemic outbreak with policies such as vaccination or reducing social contacts. This paper studies an SIHR epidemic model characterized by susceptible (S), non-seriously infected (I), hospitalized (H), and recovered (R) subpopulations, and dynamic vaccination; vaccination itself and H are fed back, and its dynamics are also determined by a free-design time-dependent function and parameters. From a theoretical analysis, the well-posedness of the model is demonstrated; positivity and the disease-free ( P d f ) and endemic ( P e e ) equilibrium points are analyzed. The controlled reproduction number ( R c ) is proved to be a threshold for the local asymptotic stability of  P d f  and the existence  P e e ; when  R c < 1  ( R c > 1 ), then  P d f  is (not) locally asymptotically stable and  P e e  does not (does) exist. Simulations have been carried out with data concerning COVID-19 where the importance of keeping  R c < 1  to prevent the disease spreading and future deaths is highlighted. We design the control input, since it can be easily adapted to match the user specification, to obtain impulsive and regular vaccination and fulfill the condition  R c < 1 . [ABSTRACT FROM AUTHOR]

In recent years, strain sensors have penetrated various fields. The capability of sensors to convert physical signals into electrical signals is of great importance in healthcare. However, it is still challenging to obtain sensors with high sensitivity, large operating range and low cost. In this paper, a stretchable strain sensor made of a double-layer conductive network, including a biomimetic multilayer graphene-Ecoflex (MLG-Ecoflex) substrate and a multilayer graphene-carbon nanotube (MLG-CNT) composite up-layer was developed. The combined action of the two layers led to an excellent performance with an operating range of up to 580% as well as a high sensitivity (gauge factor (GFmax) of 1517.94). In addition, a pressure sensor was further designed using the bionic vein-like structure with a multi-layer stacking of MLG-Ecoflex/MLG-CNT/MLG-Ecoflex to obtain a relatively high deformation along the direction of thickness. The device presented a high sensing performance (up to a sensitivity of 0.344 kPa−1) capable of monitoring small movements of the human body such as vocalizations and gestures. The good performance of the sensors together with a simple fabrication procedure (flip-molding) make it of potential use for some applications, for example human health monitoring and other areas of human interaction. [ABSTRACT FROM AUTHOR]

This paper proposes the "calm contact" technique: an imaginative scenario where someone is in gentle contact with a loved one where the essence of the experience is to enjoy safety and calmness in peaceful social contact. The theoretical background is outlined by combining the brain mechanisms of stress reactions and hypnosis. In addition to the ancient stress responses (flight or fight or freeze), there are oxytocin-based options at the human level: tend and befriend behavior and the state of calm and connection, which is not a stress reaction but a resting reaction. These social-based reactions could prevent the organism from the primitive freezing response. Some studies proved that "hypnosis" as a setting reduces cortisol levels and could raise oxytocin levels. The beneficial mechanisms of the "calm contact" technique are analyzed in relation to "social support" and the psychoaffective effects of central oxytocin. The subjective effects of the proposed technique are outlined based on reports of healthy volunteers. The "calm contact" technique could be an alternative or adjunct to the "safe place" technique, applying the recent findings of endocrinological brain mechanisms of hypnosis. Clinical implications and limitations are briefly summarized. [ABSTRACT FROM AUTHOR]

Soft robots, especially soft robotic hands, possess prominent potential for applications in close proximity and direct contact interaction with humans due to their softness and compliant nature. The safety perception of users during interactions with soft robots plays a crucial role in influencing trust, adaptability, and overall interaction outcomes in human–robot interaction (HRI). Although soft robots have been claimed to be safe for over a decade, research addressing the perceived safety of soft robots still needs to be undertaken. The current safety guidelines for rigid robots in HRI are unsuitable for soft robots. In this paper, we highlight the distinctive safety issues associated with soft robots and propose a framework for evaluating the perceived safety in human–soft robot interaction (HSRI). User experiments were conducted, employing a combination of quantitative and qualitative methods, to assess the perceived safety of 15 interactive motions executed by a soft humanoid robotic hand. We analyzed the characteristics of safe interactive motions, the primary factors influencing user safety assessments, and the impact of motion semantic clarity, user technical acceptance, and risk tolerance level on safety perception. Based on the analyzed characteristics, we summarize vital insights to provide valuable guidelines for designing safe, interactive motions in HSRI. The current results may pave the way for developing future soft machines that can safely interact with humans and their surroundings. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by impaired social communication and social interaction, restricted and repetitive behavior, and interests. The core symptoms of ASD are associated with deficits in mesocorticolimbic dopamine pathways that project from the ventral tegmental area (VTA) to the nucleus accumbens (NAc) and medial prefrontal cortex (mPFC). AST-001 is an investigational product currently in a phase 3 clinical trial for treating the core symptoms of ASD, with L-serine as the API (active pharmaceutical ingredient). Because the causes of ASD are extremely heterogeneous, a single genetic ASD model cannot represent all autism models. In this paper, we used the VPA-exposed model, which is more general and widely used than a single genetic model, but this is also one of the animal models of autism. Herein, we conducted experiments to demonstrate the efficacy of AST-001 as L-Serine that alters the regulation of the firing rate in dopamine neurons by inhibiting small conductance Ca2+-activated K+ channels (SK channels). Through these actions, AST-001 improved sociability and social novelty by rescuing the intrinsic excitabilities of dopamine neurons in VPA-exposed ASD mouse models that showed ASD-related behavioral abnormalities. It is thought that this effect of improving social deficits in VPA-exposed ASD mouse models is due to AST-001 normalizing aberrant SK channel activities that slowed VTA dopamine neuron firing. Overall, these findings suggest that AST-001 may be a potential therapeutic agent for ASD patients, and that its mechanism of action may involve the regulation of dopamine neuron activity and the improvement of social interaction. [ABSTRACT FROM AUTHOR]

Introduction: The implementation of nautical sports, particularly in coastal areas, during the school stage is a growing phenomenon. It offers unique opportunities for students to develop the necessary competencies outlined in their physical education curriculum in an immersive manner, within a natural and inherently motivating environment. Material and methods: This study aims to delve deeper into this subject by conducting a systematic review of the utilization of water sports in formal education. To carry out this bibliographic search, the following keywords were employed: "Nautical Activities", "Nautical Sports", "Nautical Camps", "Formal Education", "Children Education", "Primary School", and "Secondary School". The Boolean operator "and" was used to combine these keywords during the search conducted in databases such as Scopus, PubMed, Web of Science, and SPORTDiscus. The PRISMA Protocol was utilized for the search process, resulting in the selection and categorization of eight papers into the following thematic areas: Wellbeing, Physical and Mental Health, Education, and Management. Results: The primary findings of this review indicate that the inclusion of these programs within physical education classes enhances overall physical health, mental well-being, and personal development. Conclusions: The results demonstrate a positive impact on peer interaction and short-term improvement in self-confidence. [ABSTRACT FROM AUTHOR]

In this study, the author attempts to analyze the canons of the Orthodox Church, which are still normative for all Orthodox Autocephalous Churches, in order to identify the image of Jews and the restrictions or prohibitions imposed by these canonical texts. The paper is structured in three parts that analyze the canonical provisions regarding Jewish religious influences, Judaizing practices, Jewish–Christian religious syncretism, and social interaction with Jews based on religious influences. The main purpose of the present paper is to identify whether the Canonical Tradition of the Orthodox Church contains a form of anti-Semitism or exclusion of Jews on the basis of these texts. A well-articulated contextual interpretation of the canons is necessary to understand the relationships between Christians and Jews in the Byzantine period. To this end, the canons of the Orthodox Church will be interpreted in the social, historical, doctrinal, and canonical context of their promulgation in the life of the Orthodox Church. [ABSTRACT FROM AUTHOR]

This paper describes the main results of the JUNO project, a proof of concept developed in the Region of Murcia in Spain, where a smart assistant robot with capabilities for smart navigation and natural human interaction has been developed and deployed, and it is being validated in an elderly institution with real elderly users. The robot is focused on helping people carry out cognitive stimulation exercises and other entertainment activities since it can detect and recognize people, safely navigate through the residence, and acquire information about attention while users are doing the mentioned exercises. All the information could be shared through the Cloud, if needed, and health professionals, caregivers and relatives could access such information by considering the highest standards of privacy required in these environments. Several tests have been performed to validate the system, which combines classic techniques and new Deep Learning-based methods to carry out the requested tasks, including semantic navigation, face detection and recognition, speech to text and text to speech translation, and natural language processing, working both in a local and Cloud-based environment, obtaining an economically affordable system. The paper also discusses the limitations of the platform and proposes several solutions to the detected drawbacks in this kind of complex environment, where the fragility of users should be also considered. [ABSTRACT FROM AUTHOR]

Human–Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing processes, HRC seeks to combine the impressive physical capabilities of robots with the cognitive abilities of humans to design tasks with high efficiency, repeatability, and adaptability. During the implementation of an HRC cell, a key activity is the robot programming that takes into account not only the robot restrictions and the working space, but also human interactions. One of the most promising techniques is the so-called Learning from Demonstration (LfD), this approach is based on a collection of learning algorithms, inspired by how humans imitate behaviors to learn and acquire new skills. In this way, the programming task could be simplified and provided by the shop floor operator. The aim of this work is to present a survey of this programming technique, with emphasis on collaborative scenarios rather than just an isolated task. The literature was classified and analyzed based on: the main algorithms employed for Skill/Task learning, and the human level of participation during the whole LfD process. Our analysis shows that human intervention has been poorly explored, and its implications have not been carefully considered. Among the different methods of data acquisition, the prevalent method is physical guidance. Regarding data modeling, techniques such as Dynamic Movement Primitives and Semantic Learning were the preferred methods for low-level and high-level task solving, respectively. This paper aims to provide guidance and insights for researchers looking for an introduction to LfD programming methods in collaborative robotics context and identify research opportunities. [ABSTRACT FROM AUTHOR]