Embodied virtual agents (EVAs) are beginning to be researched to improve human–computer interaction. As EVAs become increasingly integrated into various aspects of daily life, understanding how to optimize their design to foster trust and likability among users is paramount. Leveraging insights from social psychology, particularly the concept of homophily, this study investigates the impact of perceived personality traits on user perceptions of EVAs. Specifically, we explore whether aligning the personality traits of EVAs with those of users increases engagement and fosters positive interactions. Drawing on a sample of 382 participants recruited through Amazon Mechanical Turk, we assessed participants' personality traits using the Big Five Inventory—2S, while the perceived extroversion of the agent was manipulated through facial expressions and body posture. Our findings suggest that participants were able to accurately identify the perceived extroversion of the agent (p = .014), and significant results indicate a homophily effect on trust, with participants exhibiting greater trust in agents perceived as having a similar level of extroversion (p < .01). However, no significant effect on likability was detected, suggesting a more nuanced relationship between perceived personality traits and user preferences. These findings highlight the potential of leveraging homophily in designing more engaging EVAs and underscore the importance of considering user–agent compatibility in human–computer interaction.

Behavioral monitoring tools can be proven to be especially helpful for aging workers for whom it is of paramount importance to avoid sedentary lifestyle, decreasing the possibility to exhibit musculoskeletal, cardiovascular and other health/mental related problems, which could impede their workability and job performance. Towards this direction, we have developed an unobtrusive pervasive health monitoring framework, enabling activity and location tracking, as well as self-reporting. The collected data is used to provide meaningful health-related suggestions through a life-like Virtual Coach aiming to encourage the engagement of workers with healthy lifestyle and behaviors. In this paper, we present the design and functionality of the solution, which was implemented as a mobile app accompanied with a dashboard for data overview, and demonstrate the results of its acceptance through a conducted survey. Survey participants were asked to answer questions related to their work experience, frequency of use of technology and type of applications used, well-being and satisfaction at work as well as their data privacy concerns. Finally, the participants also answered questions about the level of acceptance of the system and the perceived usefulness. Within the survey, video demonstrations and images were included, so that the participants have the chance to receive a better understanding of solution functionality. The acceptability study was conducted with the participation of 52 people, from which 90.4% reported that they found the Virtual Coach recommendations useful, while 84.6% reported intention of using the proposed solution in their daily life. The results indicate the virtue of such tools in promoting worker well-being.

Pervasive health monitoring tools and applications based on mobile devices can be used for digital phenotyping, thereby facilitating diagnosis and treatment of diseases. We present the development of an unobtrusive health monitoring framework, enabling multimodal data collection of activity information from wearable devices, smartphones, and third-party apps, keystroke typing data, location information, data from medical and environmental IoT devices, and self-reports. The architectural elements of the framework are presented, and proof-of-concept case studies based on the developed framework are demonstrated, to show its feasibility and value in building different pervasive health applications.

The concept of modeling the behavior of industrial processes is of great importance as it describes the possible states of equipment used in large industries, which once damaged, it usually costs both in time and money. In this paper, we propose a data-driven methodology for depicting three distinct states of a chemical reactor, (1) normal, (2) warning, (3) alert, by using machine learning techniques. A method for predicting the classification of data input, assists in prevention (early prognosis) of possible malfunctions. This method uses a combined linear trend analysis of the involved data which form the warning state of the reactor where the pre-incident conditions are fulfilled. Afterwards, it checks the possibility of the subsequent input to be classified in the alert state which is an indication that the reactor’s active equipment, such as heating resistance, will start malfunctioning. The objective of the three main steps of the proposed methodology are: first, to reveal the number of clusters based on past data, second to train normal, warning and alert behavior-models and validate them and third to test them as well as verify the accuracy of linear trend analysis. The proposed methodology is based on the analysis of real data sets​ derived from the automation system of a chemical process located at CERTH/CPERI in order to identify real-life models for prognostic behavior for malfunction prevention. This approach is especially suitable for modern industrial systems that follow Industry 4.0 principles. The results reveal a robust modeling of the reactor’s behavior with accuracy reaching 88,94%.

Nowadays a lot of people working in industrial environments face daily routines as boring and dull which disaffects them from each other. Thus, the evolution of online communities has given a great opportunity in communication and interaction between people that work in the same environment but may not have direct interaction. An online Social Collaboration platform introduced to industrial environment enhances interaction between colleagues and empowers bonding by creating a social community where participants can share concerns, ideas and knowledge. Moreover, when introducing a new idea to a workplace, like Social Collaboration platform, it is expected to have both positive and negative reactions so it is challenging to create an attractive environment and engage as many individuals as possible. For this reason a gamification strategy can be used which is proven to appeal to the majority of people when applied with appropriate strategy. The gamified social collaboration platform that is introduced in this paper supports engagement of employees in to their daily jobs by triggering motivation and offering only positive feedback. The objective of the proposed concept is the creation of a pleasant and friendly environment in industrial premises where employees have the need to participate in collective activities and improve team spirit. A complete social collaboration tool with gamified actions is presented which is deployed in a plant floor at CERTH/CPERI and tested on real conditions during 3 months. The analysis of the user's engagement is promising and showed that the platform was successfully introduced at daily procedures and interactions using the gamified actions. Moreover, an overall enhancement of positive mood between colleagues is noticed along with engagement and satisfaction. This novel social platform proves that there is a necessity for change in industries by boosting human contact and eliminating alienation. Fortunately, with the right use of technology, this necessity can be satisfied and the whole procedure of communication, engagement and knowledge exchange is facilitated.

This paper presents gamification concepts implemented by a gamification engine which is incorporated in a knowledge sharing web-based application. The engine aims at increasing user’s motivation and participation in knowledge sharing and training processes taking place on a factory’s shop floor, enhance socialization and support corrective feedback and positive reinforcement. In particular, it motivates workers to participate in discussions, propose solutions to work-related problems, and upload/view useful content even when being at the workplace. The gamification engine makes use of various gamification elements and is highly configurable in terms of management of gamified tasks. It is designed to support access by both standard display devices (PCs, tablets, mobile phones) as well as Mixed/Augmented Reality platforms, such as Microsoft HoloLens, which are gaining significant traction with industry verticals. The main novelty of the gamification concepts presented is the ability to utilize dynamic worker profile information which is stored in a central database, in order to improve the effectiveness of the gamified tasks, targeting at more effective usage of the knowledge sharing platform in the Industry 4.0 domain.

This study evaluates user acceptance of a gamification-enabled collaboration and knowledge sharing platform that has been developed for use by personnel in industrial work environments, aiming at increasing motivation for knowledge exchange. The platform has been evaluated at two manufacturing industries by two groups of users, workers and supervisors, with regard to five criteria: usability, knowledge integration, working experience, user acceptance and overall impact. Results showed that even though the ratings from both industries were positive on all criteria, there is room for improvement on user acceptance and knowledge integration. Driven by this fact, a rule-based adaptive gamification approach which exploits information about workers is proposed in order to further increase motivation and engagement. Based on feedback received from the evaluation, guidelines related to functionalities and design of a gamified collaboration platform are provided. These guidelines can be followed when implementing collaboration tools with gamification support for industrial environments.

This paper presents a gamified framework designed to offer behavioural change support and treatment adherence services to people living with Dementia (PLWD), their caregivers and medical/social professionals. A flexible and scalable ICT solution architecture was proposed to support highly personalized and gamified services for all groups involved: cognitive skills training and independent living for PLWD, training and support for caregivers and high clinical and social services for professionals. The outcomes of this approach are delivered through a set of gamification concepts running in parallel to create motivation for user commitment and for achieving the desired behavioral change. After projecting all user group expectations on a social game canvas, the impact evaluation will assess the intended effects of the proposed gamification approach on the welfare on PLWD and their caregivers.

The determination of abnormal behavior at process industries gains increasing interest as strict regulations and highly competitive operation conditions are regularly applied at the process systems. A synergetic approach in exploring the behavior of industrial processes is proposed, targeting at the discovery of patterns and implement fault detection (malfunction) diagnosis. The patterns are based on highly correlated time series. The concept is based on the fact that if independent time series are combined based on rules, we can extract scenarios of functional and non-functional situations so as to monitor hazardous procedures occurring in workplaces. The selected methods combine and apply actions on historically stored, experimental data from a chemical pilot plant, located at CERTH/CPERI. The implementation of the clustering and classification methods showed promising results of determining with great accuracy (97%) the potential abnormal situations.

Supporting the use of technology into industrial environments is an issue of mass appeal within the Industry 4.0 initiative, with a lot of promising research especially into interaction, production and training sections. In this paper, a Social Collaboration platform is introduced which creates an online community for workers as well as an Augmented Reality (AR) tool for production and training purposes both of which constitute gamified applications on which a customizable Gamification platform is applied according to impending needs. These tools have been implemented in order to become of daily use to employees in factories and incentivize them to promote collaboration, engagement, participation and work satisfaction. Every promoted industrial behavior can be described and awarded based on the offered rule engine. Thus, two gamified processes are offered regarding Social Collaboration and AR training to employees of industrial environments, so that an inference is drawn concerning participation, satisfaction and self-fulfillment. The use of the platforms is illustrated in this paper by two examples consisting one use case for a section in social collaboration and a second use case of training through AR.