Electrical muscle stimulation (EMS) can create compelling weight sensations in virtual reality by activating antagonist muscles, prompting users to exert effort against induced contractions. This experiment investigates personalizing EMS amplitudes to precisely make a 2 kg object feel 1 kg heavier. This task is challenging due to high individual variability in sensory, motor, and pain thresholds, as well as physical characteristics. We applied nine personalized EMS intensity levels to the triceps and extensor carpi ulnaris muscles of 75 participants, who then compared the perceived weight of the stimulated arm against a 3 kg reference. A machine learning model was trained on the collected data to predict the perceived weight difference from user-specific features. The best-performing XGBoost model achieved a classification accuracy of 77%. Feature analysis revealed that the applied EMS amplitude was the most influential predictor, supplemented by individual motor and pain thresholds, body mass index, and forearm length. This ML-based personalization is a significant advancement for creating adaptive haptic feedback, with applications in immersive VR, physical rehabilitation, and physiotherapy.

Regulations for privacy protection aim to protect individuals from the unauthorized storage, processing, and transfer of their personal data but oftentimes fail in providing helpful support for understanding these regulations. To better communicate privacy policies for smartwatches, we need an in-depth understanding of their concepts and provide better ways to enable developers to integrate them when engineering systems. Up to now, no conceptual model exists covering privacy statements from different smartwatch manufacturers that is reusable for developers. This paper introduces such a conceptual model for privacy policies of smartwatches and shows its use in a model-driven software engineering approach to create a platform for data visualization of wearable privacy policies from different smartwatch manufacturers. We have analyzed the privacy policies of various manufacturers and extracted the relevant concepts. Moreover, we have checked the model with lawyers for its correctness, instantiated it with concrete data, and used it in a model-driven software engineering approach to create a platform for data visualization. This reusable privacy policy model can enable developers to easily represent privacy policies in their systems. This provides a foundation for more structured and understandable privacy policies which, in the long run, can increase the data sovereignty of application users.

Older adults (OA) increasingly use smartphones, which provide visual, auditory, and haptic signals. However, it remains unclear if multimodality's positive effects on performance during controlled experiments and driving scenarios apply to OA smartphone interaction. Multilevel models showed that reaction times (RT) of 18 younger adults (YA) and 15 OA to unimodal stimuli$(\mathbf{M}=1,178\ \mathbf{ms})$are significantly higher$[\mathbf{F}(2,5_{9}308.7)=69.17, \mathbf{p} <. 001]$than to bimodal$(\mathbf{M}=1,110\ \mathbf{ms};\ \mathbf{do}\ =0.26)$or trimodal stimuli$(\mathbf{M}$= 1,071;$\mathbf{do} =0.42)$. The difference between unimodal$(\mathbf{M}=1,307)$and bimodal stimuli$(\mathbf{M}=1,216;\ \mathbf{do}\ =0.35)$and between unimodal and trimodal stimuli$(\mathbf{M}=1,188: \mathbf{do} =0.46]$), was greater for OY than YA$[\mathbf{F}(2,5_{2}308.7)=3.77;\mathbf{p}=.023]$. Results thus confirm findings from aging and multimodal information processing literature and suggest using more than one feedback modality to improve OAs smartphone interaction.

The vision of the Internet of Production (loP) is focused on optimizing manufacturing processes, with the help of Industry 4.0 technologies (14Ts). However, considering global megatrends such as climate change and the need to achieve the Sustainable Development Goals (SDGs), it is a growing imperative for the manufacturing industry to become more sustainable. This opens the door to transforming the IoP into an Internet of Sustainable Production (loSP). Accordingly, this paper proposes a novel four-step loSP-framework that establishes an Information System (IS) allowing researchers and practitioners to acknowledge and adapt to the interconnected nature of sustainability. Further, to test its applicability, an inter- and cross-disciplinary perspective is adopted to illustrate case-related challenges, opportunities, and pathways - revealed by the proposed framework - in the example of a digital economy for sustainability data, strategic design of global production networks, human-robot collaboration, digital photonic production, and the textile industry. Together, the framework demonstrates the usefulness of generating holistic information on the interconnected nature of sustainability derived from process-specific and contextualized data, while simultaneously assessing the framework's utilization for sustainability, as well as the sustainability of its use.

Digital shadows play a central role for the next generation industrial internet, also known as Internet of Production (IoP). However, prior research has not considered systematically how human actors interact with digital shadows, shaping their potential for success. To address this research gap, we assembled an interdisciplinary team of authors from diverse areas of human-centered research to propose and discuss design and research recommendations for the implementation of industrial user interfaces for digital shadows, as they are currently conceptualized for the IoP. Based on the four use cases of decision support systems, knowledge sharing in global production networks, human-robot collaboration, and monitoring employee workload, we derive recommendations for interface design and enhancing workers’ capabilities. This analysis is extended by introducing requirements from the higher-level perspectives of governance and organization.

High levels of mental workload can precipitate an inability to cope with job demands, increase the risk of serious mental and physical health issues, as well as contribute towards long-term sick leave, or early retirement. Monitoring health risk factors using psychophysiological measurement methods can raise individuals’ awareness of changes in their vital signs and optimal individual workload range. However, the perceived usefulness and the resulting acceptance is imperative for the implementation of such technologies at the workplace. Hence, a study was conducted that aimed at providing insight into how psychosocial demands at work affect the perceived usefulness of mental health risk monitoring systems and how this influences the behavioral intention to actually use such systems. For this purpose, an online survey was conducted with N=493 office workers. Results indicate that a direct positive relationship between quantitative demands and perceived usefulness of health monitoring technologies, as well as an indirect positive relationship with behavioral intention exits. The results can help to understand the factors influencing the acceptance of technologies for monitoring occupational health risks, thus facilitating the use of technologies for health promotion at the workplace.

While user-centered design philosophy and corresponding design recommendations are central pillars of human-robot interaction (HRI) research, the process how to move from such abstract and generalized design recommendations to concrete, context-specific design implementations remains under-researched and vague in the literature. The goal of this paper is therefore to show an approach for moving from abstract design recommendations to a concrete interface, and thus illustrates a design process that is rarely illustrated in concrete terms in HRI. This is done using a real-world use case of designing a possible user-centered interface for mobile cooperative manufacturing robots for assembly work in a medium-sized company. A study is presented to conceptualize and test a Research-through-Design approach, which combines transdisciplinary methods to determine relevant information which should be displayed on a graphical user interface (GUI) for HRI. Based on the use case, a Goal-Directed Task Analysis (GDTA) was conducted, consisting of a participatory observation and interviews with subject matter experts to analyze an assembly task from the work objective to the information units. The acquired information has been transferred to a physical model. A wireframe has been created to show how the results of the GDTA and the physical model can be applied to a GUI. The wireframe design has been evaluated through qualitative interviews with end users (n = 12) to get first estimates about its relevance. In order to validate the applied methods, design and engineering students (n = 10) repeated the process in stages followed by interviews. The results indicate that the method mix shows potential and leads to supportive user interfaces.

In recent years, production systems have become highly sophisticated and complex. As a result, while on the one hand, the least-skilled labor has been partially displaced by machines, high-skilled labor is more required to supervise and control advanced automation systems. In many cases, the complexity of machines implies an increased complexity of human–machine interfaces (HMIs), which are the main point of contact between the operator and the machine. To enable effective use of HMIs and to enable their usage by workers with different knowledge and capabilities, novel design approaches have been proposed. In particular, in this article, we consider the approach developed in the framework of the European research project INCLUSIVE, which aimed at designing industrial HMIs that adapt to the skills and capabilities of human operators. As a case study, we consider an adaptive interaction system for the woodworking industry and present an extensive evaluation carried out in real production environment with shopfloor workers. The effectiveness of the INCLUSIVE approach has been assessed with subjective and objective measurements and compared to that of interaction systems customarily used in industry. Results have shown that users appreciated the INCLUSIVE system and largely preferred it over the customary system. Moreover, with regard to objective performance-related measurements, they performed better when using the INCLUSIVE system since they received tailored guidance during the considered working tasks. Note to Practitioners— This article was motivated by the fact that advanced automation systems are often highly complex for human operators. To address this problem, we focus on the importance of designing the automation system around users and discuss an approach for adaptive automation, called INCLUSIVE. Its main feature is that it adapts the human–machine interface (HMI) according to operator’s skills, capabilities, and current mental fatigue. In this article, we provide an extensive evaluation of the INCLUSIVE system, considering a company producing woodworking machines as a use case. Assessment was carried out in the company shopfloor, considering real workers, and the INCLUSIVE system was compared with the customary HMI running on the company machines. The results of our study suggest that adapting the interaction to operator’s needs allows better working performance while letting workers more satisfied with the use of the system.

A recent literature review on personal space in human-robot interaction identified a research gap for the influence of contextual factors. At the same time, psychological research on interpersonal distancing and theoretical considerations based on compensatory control models suggest the importance of considering these factors in robot path planning. To address this gap, we tested the effect of room size and working memory load on participants’ comfort distance toward an approaching robot. In a preregistered 3 × 2 within-subject design, N = 72 participants were approached by a mobile manufacturing robot in a corridor with varying room size and with and without a cognitive secondary task. As dependent variables, comfort distance, arousal, and perceived control were measured. While room size and working memory load had no significant direct effect on comfort distance, participants felt higher arousal and lower control in smaller rooms and in conditions with high working memory load, which in turn caused larger comfort distances (indirect effect). With experience, comfort distances decreased. Based on the indirect effects, future studies should test the effect of more extreme manipulations on comfort distances. Robots should adapt their path planning by keeping larger distances toward human workers in stressful environments to avoid discomfort.

Digital Shadows as the aggregation, linkage and abstraction of data relating to physical objects are a central vision for the future of production. However, the majority of current research takes a technocentric approach, in which the human actors in production play a minor role. Here, the authors present an alternative anthropocentric perspective that highlights the potential and main challenges of extending the concept of Digital Shadows to humans. Following future research methodology, three prospections that illustrate use cases for Human Digital Shadows across organizational and hierarchical levels are developed: human-robot collaboration for manual work, decision support and work organization, as well as human resource management. Potentials and challenges are identified using separate SWOT analyses for the three prospections and common themes are emphasized in a concluding discussion.

While modern production systems are becoming increasingly technologically advanced, the presence of human operators remains fundamental in industrial workplaces. To complement and enhance the capabilities of human workers, approaches based on adaptive automation have been introduced. They consist of adapting the behavior of the system according to the user’s capabilities and effort. In this article, we present a general holistic framework for adaptive automation, called INCLUSIVE, that assists the operator during working tasks. The system consists of three modules. First, a thorough characterization of the operator’s constitutional and situational condition is provided; based on this, properly tailored adaptation is given, and if necessary, further training and support are provided. The framework has been implemented and tested considering three industrial use cases, selected as representative of a wide area of interest for the industry in Europe, in terms of both production requirements and involved operators. Tests have been carried out in real production environments, considering real production tasks carried out by 53 shop-floor workers. Results have shown that workers’ satisfaction when using the INCLUSIVE system and their performances was increased with respect to customary interaction systems currently used in industries. Moreover, the achieved results were used to formulate a set of recommendations for the design and implementation of an adaptive interaction system in relation to ensuring worker satisfaction and system usability in an industrial environment, as well as performance requirements.Note to Practitioners—This article was motivated by the fact that, despite modern advanced automation, human operators are still central in the manufacturing process. However, technological progress often causes challenging interaction with complex industrial systems. The goal of this article is to introduce a complete framework for adaptive automation, with the ultimate goal of facilitating the interaction of human operators with complex industrial systems. The framework relies on three modules: measurement of human capabilities, the adaption of the interaction system, and additional teaching and support. The three modules are discussed at a high level, independently of the target application. Moreover, to facilitate their application in specific working contexts, examples are provided with respect to three different industrial applications. Results of tests carried out with shop-floor operators show that implementing the proposed framework allows better working performance and increases worker satisfaction with the use of automation.

Human-machine systems for identifying and defusing improvised explosive devices need to be designed according to specific user requirements and task-specific affordances. The navigation of mobile robots in unknown environments is, for example, a typical task of emergency response personnel, which occurs during situation assessment and analysis of suspicious objects. This contribution investigates determinants of the efficient performance as well as human behaviour in remote navigation tasks. In an experimental study, participants had to navigate forwards and backwards in a virtual 3D maze, then draw the path they had covered from memory. Task complexity and environmental stressors were varied between levels, to examine their impact on performance (task completion time, route retracing performance) and subjective measures (mental workload, perceived difficulty). In a sample of 50 participants, a negative relationship between complexity and the dependent variables was found. For the stressors, only the addition of an acoustic stressor had an impact on the performance measures. A discussion of the practical implications of these results is provided.

When full automation of mobile robots is not possible or desirable, teleoperation constitutes an alternative. The human operator can be supported with direction cues to facilitate localization or navigation. These cues are presented typically in the auditory, haptic and/or visual modality. An experiment was conducted to evaluate systematically and empirically the (uni-modal and multi-modal) effects of auditory and haptic feedback compared to visual feedback on target localization accuracy. Results show that haptic as well as auditory direction cues lead to significantly lower accuracy than visual cues. Moreover, combining feedback cues does not necessarily lead to better performance and can even reduce accuracy. Based on the results, possible implications for multi-modal human machine interface design are discussed.

The act of revealing personal information, thoughts, and feelings is known as self-disclosure. Self-disclosure represents an important determinant of liking and is central to the development of close relationships among humans. The present study aimed to investigate the role of self-disclosure in human-robot interaction (HRI). 81 participants were randomly assigned to one of four experimental conditions in which they interacted with the humanoid robot NAO. We manipulated whether the robot disclosed personal information or whether the robot asked personal questions to the human interaction partner, so that the participant had to self-disclose. In two control conditions the robot either made factual statements or the robot asked factual questions. Contrary to the hypotheses, the results indicated no immediate statistically significant effects of self-disclosure on the dependent variables robot likability, human-robot interaction quality, future contact intentions, and mind attribution. However, when taking into account participants' tendency to anthropomorphize technology, nature, and the animal world as a covariate, self-disclosure was found to significantly affect participants' tendency to attribute mind to NAO. Furthermore, the results indicate that the form of engagement and dominance in an interaction (i.e., being in the role of a passive listener vs. conversing actively) may affect perceived HRI more than does the content of the verbal exchange. Thus, the paper highlights the importance of considering covariates (i.e., interindividual differences in the tendency to anthropomorphize nonhuman entities) in HRI analyses and points out possible relevant moderators of self-disclosure in HRI.

Many envision a future in which personal service robots share our homes and take part in our daily lives. These robots should possess a certain “social intelligence”, so that people are willing, if not eager, to interact with them. In this endeavor, applied psychologists and roboticists have conducted numerous studies to identify the factors that affect social interactions between humans and robots, both positively and negatively. In order to ascertain the extent to which the social human-robot interaction might be influenced by robot behavior and a person's attitude towards technology, an experiment was conducted using the UG paradigm, in which participants (N=48) interacted with a robot, which displayed either animated or apathetic behavior. The results suggest that although the interaction with a robot displaying animated behavior is overall rated more favorably, people may nevertheless act differently towards such robots, depending on their perceived technological competence and their enthusiasm for technology.