Production environments are characterized by an increasingly diverse workforce caused by demographic change, globalization, and the rising demand for inclusion and equality. To ensure employee satisfaction for such a diverse workforce, gamification is a promising method. However, one-size-fits-all approaches are not sufficient, and more user-adaptive and inclusive gamified systems are needed. Therefore, this systematic literature review aims to answer the question of which user-adaptive gamified systems exist for manufacturing and how they provide inclusivity for individuals in their everyday work. Following PRISMA guidelines, a search of five electronic databases retrieved 22 relevant articles. Analysis of the literature revealed a lack of user-centered and inclusive gamified systems. Further, limited empirical evaluations in real production environments, limited application scenarios, and the need for more diverse research were identified. Based on the results, this study identifies key research gaps and provides recommendations for future research.

Digital assistive technologies (ATs) have been widely used to support people with disabilities at work.However, many existing systems, interfaces, and tools remain inaccessible or insufficiently adaptable to the wide range of human abilities, particularly when cognitive, communicative, or sensory differences are involved.This gap is further exacerbated by what scholars and activists refer to as the disability divide: the sociotechnical disparity between people with and without disabilities in terms of access to, use of, and benefits from digital technologies.Despite increasing policy efforts and legal frameworks, vocational inclusion and training remains a significant challenge.By bringing together a diverse community, this workshop seeks to critically examine the role of digital ATs in advancing vocational inclusion for individuals with disabilities. CCS Concepts• Human-

Recent HCI research has shown significant interest in investigating digital working instructions for guiding novices to perform manual tasks. While performance enhancement has been a primary focus, it is increasingly recognized that technology’s impact extends beyond objective metrics. Trainee motivation and engagement plays a pivotal role in enhancing learning outcomes and effectiveness. This paper investigates the utilization of principles from Self Determination Theory–clear attainable goals, meaningful rationale, and perspective taking–in designing multimedia instructions to enhance novice users’ indicators of psychological well-being. We present findings from an experiment involving real-world woodworking, where novice users, in a between-subjects study, followed interactive, in-situ projection-based guidance. Results demonstrate that adhering to SDT postulates can positively influence perceived competence, intrinsic motivation and task execution quality. These findings offer valuable insights for designing digital instructions to guide and train novices, emphasizing the importance of psychological well-being alongside task performance.

Past research comparing augmented reality (AR) media such as in-situ projection and head-mounted devices (HMD) has usually considered simple manual activities. It is unknown whether previously reported differences between different AR media also apply to complex, skill-driven tasks. In this paper, we explore the feasibility and challenges in designing AR instructions for expertise-driven, skilled activities. We present findings from a real-world, between-subjects experiment in which novices were instructed to trim and bone sub-primal cuts of pork using two interactive AR prototypes, one utilizing in-situ projection and a second using the Hololens 2. The prototypes and instructions were designed in consultation with experts. We compared novices' task performance and subjective perceptions and gathered experts' feedback. Although both users and experts indicated a subjective preference for in-situ projection, results indicate that when tasks require knowledge, skill and expertise, the choice of the AR medium itself may not be consequential. Rather, in our experiment, the instruction quality influenced comprehension, knowledge retention and task performance. Hence, from an engineering perspective, emphasis ought to be laid on gathering and structuring expert performance and knowledge to create effective instructions, which could be delivered using any AR medium suited to the task and work environment.

In automated driving (AD), high technology and individual users connect. They do so via user interfaces (UI). Since every human differs significantly in preferences and requirements towards UIs and the information shown, we now face the challenge to meet these individual requirements to enhance the customer experience in AD. We showed in two driving simulator studies that differences in information preferences for the UI are connected to differences in user characteristics. Based on this, we now create a set of design principles (DP) including its mathematical modeling that helps to translate user characteristics into personalized interface configurations. As user characteristics, we incorporate experience with AD, driving frequency, sight impairs, and attitude towards technology. For information, we chose surrounding traffic, surrounding infrastructure, and potential limits of the AD system. We hope that the DPs contribute to theoretical knowledge and future vehicle development processes fostering users’ feelings of safety, comfort, and satisfaction.

Recent studies on assistance systems show that adaptive support for users is becoming increasingly important, as this is a promising approach to ensure acceptance, learning support, and appropriate assistance in the long term. Developers of assistance systems are therefore faced with the challenge of designing various dimensions of adaptivity in such a way that they are conducive to acceptance and support. In this paper, we consider the different dimensions and options for the adaptation of assembly assistance and present the results of a first survey to investigate users’ expectations regarding the adaptability of assembly assistance systems. Our evaluation shows that users have clear preferences concerning the control over adaptation and adaptation goals, while other aspects of adaptation assistance do not indicate a clear tendency. The results of this survey should serve as a decision support in the design phase of adaptive assistance systems to choose between different implementation options and concepts.

Worker assistance systems (AS) are a promising way to support workers in complex production processes or unfamiliar manual activities. In order to leverage their full potential, the information these systems provide have to be adapted to the specific needs of the users and the underlying production processes. With shorter product lifecycles and increasing process variability, the time and knowledge required to regenerate the instructional content becomes a crucial success factor. In this paper, we introduce the concept of low-code development as an alternative approach to existing ways of generating content for AS. To validate our concept, we implemented an editing tool based on node graphs, which enables end users to develop AS for a wide range of industrial processes without the need of any expertise in software engineering. This way, the end users, comprised of shop-floor staff and domain experts, can utilize their extensive production know-how that is required for the implementation of AS in elaborate production systems. Empirical evidence gained by developing two AS for different industrial processes showed that the proposed graph-based approach is faster, more flexible and offers better usability than existing solutions.

Automated driving (AD) has the potential to change our mobility by giving us back the riding time for our free disposal. Since this aims at a broad base of customers, the people using such technology can strongly differ. Differences can comprise for instance prior AD experience, driving frequency, or physical impairments. In this paper, we investigate which effects these user characteristics have on the preferences regarding displayed surrounding traffic information. We do so by using a moving-base simulator study with 58 participants. We found that drivers with experience with automatic cruise control perceive information regarding surrounding traffic as significantly less important, while users with higher driving frequency or sight impair found them significantly more important. For theory this implicates that different user interfaces for different users are ideal and personalization can foster user experience.

Digital assistive systems, enable workers with disabilities to perform complex industrial work. However, the previously presented systems considered only a single workplace and a single user. This paper presents an assembly line that enables a joint processing of complex tasks by multiple workers with and without disabilities. The aim was to investigate the use of interaction technologies such as in-situ projections and hand-tracking to enable the processing of complex assembly tasks by work teams with highly heterogeneous abilities. The developed assembly line assists users and coordinates the joint work by distributing single assembly steps to workers based on the individual workers' abilities. Besides presenting the concept and implementation of the assembly line, we report our findings after six months of operation. Our results indicate that using the assistive assembly line has positive impacts, such as increased satisfaction and independence of the workers combined with a higher productivity.

Augmented Reality (AR) systems are on their way to industrial application, e.g. projection-based AR is used to enhance assembly work. Previous studies showed advantages of the systems in permanent-use scenarios, such as faster assembly times. In this paper, we investigate whether such systems are suitable for training purposes. Within an experiment, we observed the training with a projection-based AR system over multiple sessions and compared it with a personal training and a paper manual training. Our study shows that projection-based AR systems offer only small benefits in the training scenario. While a systematic mislearning of content is prevented through immediate feedback, our results show that the AR training does not reach the personal training in terms of speed and recall precision after 24 hours. Furthermore, we show that once an assembly task is properly trained, there are no differences in the long-term recall precision, regardless of the training method.

Computer-aided assistance systems are entering the world of work and production. Such systems utilize augmented- and virtual-reality for operator training and live guidance as well as mobile maintenance and support. This is particularly important in the modern production reality of ever-changing products and `lot size one' customization of production.This paper focuses on the application of machine learning approach to extend the functionality of assistance systems. Machine learning provides tools to analyse large amounts of data and extract meaningful information. The goal here is to recognize the movement of an operator which would enable automatic display of instructions relevant to them.We present the challenges facing machine learning applications in human-centered assistance systems and a framework to assess machine learning approaches feasible for this scenario. The approach is assessed on a historical data set and then deployed in a work station for live testing. The post-hoc, or historical, analysis yields promising results. The ad-hoc, or live, analysis is a complex task and the results are affected by multiple factors, most of which are introduced by the human influence.The contribution of this paper is an approach to adapt state- of-the-art machine learning to operator movement recognition with a special focus on approaches to spatial time series data pre-processing. Presented experiment results validate the approach and show that it performs well in a real-world scenario.

The term Industrie 4.0 carries the vision of smart factories, which automatically adapt to changes and assist the human as much as possible during operation and maintenance. This includes smart human machine interfaces, which reduce the chances of errors and help to make the right decisions. This paper presents an approach to equip the maintenance software running on a tablet PC with augmented reality functionality to be able to place virtual sticky notes at production modules. Additionally, these sticky notes are enriched with position information. The central element of this approach is an ontology-based context-aware framework, which aggregates and processes data from different sources. As a result, a tablet PC application was implemented, which allows displaying maintenance information as well as live plant process data in the form of augmented reality. More than 100 of those sticky notes can be placed using this system, whereas each note requires a file size of 12 to 16 kilo bytes. After placing a sticky note, the system recognizes it even if the camera's position is not exactly the same as during the placing process.

This paper presents a novel approach for mediating awareness in small intimate groups. Instead of traditional communication media, music is used to inform users about the presence and mood of multiple remote peers. Based on this conceptual idea, an awareness system called 'Social Radio' was developed. The system consists of several smart artifacts and an underlying multi-user communication infrastructure.

In this paper a music-based awareness system called 'Social Radio' is presented. The system focuses on small intimate groups and enables multiple persons to stay in touch using smart artifacts and tangible interaction mechanisms.