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.

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.

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.

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.

We introduce a novel use of wearable technology to reconstruct and output high dynamic range (HDR) audio signals. The purpose of designing an end-to-end HDR audio wearable device is to create an audio device capable of replicating and even extending the dynamic range of human hearing. We also introduce a different method of visualizing audio by way of a rotary Sequential Wave Imprinting Machine (SWIM). The results show that real-time HDR audio processing and visualization are possible at the standard audio sampling rate of 44.1 kHz. Additionally, the quality of the recorded audio improved from the originally captured data after HDR processing. We further analyze the dependence of different audio gain combinations as well as the influence of the number of audio input channels on the quality of the HDR signal reconstruction in various acoustic environments.

We describe an open-source software framework,CGRA-ME, for the modeling and exploration of coarse-grained reconfigurable architectures (CGRAs). CGRAs are programmable hardware devices having large ALU-like logic blocks, and datapath bus-style inter-connect. CGRAs are positioned between fine-grained FPGAs and standard-cell ASICs on the spectrum of programmability - they are less flexible than FPGAs, yet are more flexible than ASICs. With CGRA-ME, an architect can describe a CGRA architecture in an XML-based language. The framework also allows the architect to map benchmarks onto the architecture and provides automatic generation of Verilog RTL for the modeled architecture. This allows the architect to simulate for verification purposes, and perform synthesis to either an ASIC or FPGA-overlay implementation of the CGRA, assessing performance, area, and power consumption. In an experimental study, we use CGRA-ME to model, map benchmarks onto, and evaluate several variants of a widely known CGRA, considering both standard-cell and FPGA-overlay physical realizations of the CGRA.

Modern manufacturing systems typically require high degrees of flexibility, in terms of ability to customize the production lines to the constantly changing market requests. For this purpose, manufacturing systems are required to be able to cope with changes in the types of products, and in the size of the production batches. As a consequence, the human-machine interfaces (HMIs) are typically very complex, and include a wide range of possible operational modes and commands. This generally implies an unsustainable cognitive workload for the human operators, in addition to a non-negligible training effort. To overcome this issue, in this paper we present a methodology for the design of adaptive human-centred HMIs for industrial machines and robots. The proposed approach relies on three pillars: measurement of user's capabilities, adaptation of the information presented in the HMI, and training of the user. The results expected from the application of the proposed methodology are investigated in terms of increased customization and productivity of manufacturing processes, and wider acceptance of automation technologies. The proposed approach has been devised in the framework of the European project INCLUSIVE.

According to recent studies, cognitive processes are modulated by the proximity of the hands to a stimulus. Specifically, hand proximity (also known as nearby-hand or hand-presence effects) induces a bias to process information near the hands more precisely and this effect can be facilitative or debilitative depending on the task context. Two different distances of the hands in reference to the screen were studied as independent variables: hands placed on the screen and hands placed on the lap. The dependent variables were search times and different eye-tracking parameters. Given the age-related decline in the perception of peripersonal space, the results were analysed for two different age groups. Overall, we found a more detailed evaluation of information near the hands depending on age. In conclusion, the study presents a cognitive behavioural evaluation of human–computer interaction which can be used for touchscreen interface and interaction design as well as modelling human–system interaction.

Elderly users suffering from hand tremor have difficulties interacting with touchscreens because of finger oscillation. It has been previously observed that sliding one's finger across the screen may help reduce this oscillation. In this work, we empirically confirm this advantage by (1) measuring finger oscillation during different actions and (2) comparing error rate and user satisfaction between traditional tapping and swabbing in which the user slides his finger towards a target on a screen edge to select it. We found that oscillation is generally reduced during sliding. Also, compared to tapping, swabbing resulted in improved error rates and user satisfaction. We believe that swabbing will make touchscreens more accessible to senior users with tremor.