Open Access

Musculoskeletal disorders (MSDs) are prevalent among musicians, posing challenges for both musicians and physiotherapists. Clinical Movement Analysis (CMA) permits the accurate and objective assessment of posture and movement during musical performance, which can enhance MSD diagnosis. However, tools for CMA data analysis that are tailored to physiotherapy workflows are lacking. The aim of this study was to design an interactive tool that integrates CMA data to assist physiotherapists in diagnosing musician-specific MSDs, addressing limitations of traditional diagnostic methods. Following a user-centered design process and visualization design model, the tool was iteratively refined with expert feedback. It meets six key requirements by integrating biomechanical data (i.e., joint rotation angles and muscle activity) with clinical findings to support hypothesis-driven assessments. The prototype features an intuitive, mobile-friendly interface, interactive visualizations of time-oriented data, and reference values for detecting abnormalities. It enables efficient analysis of posture and movement patterns while linking findings to diagnostic hypotheses. Preliminary evaluations have highlighted its usability, alignment with physiotherapy workflows, and potential to bridge the gap between CMA data and clinical decision-making. By enhancing MSD diagnosis and treatment planning, the tool promises to improve patient outcomes. Future work involves clinical validation, broader application to diverse musician populations, and the incorporation of advanced analytics, including machine learning, to further enhance the tool’s capabilities.

This study investigates factors influencing the perceived ease of use (PEOU) of AI-camera systems among German pig farmers. AI-based surveillance systems support tasks such as animal detection, tracking, behavior analysis, and disease diagnosis, but their adoption is hindered by concerns over data privacy and usability. Using the Technology Acceptance Model (TAM) as a foundation, the study explores three factors: perceived risk of data abuse (RI), perceived property rights of data (PR), and perceived transparency (TR). Survey data from 185 pig farmers were analyzed using partial least squares structural equation modeling (PLS-SEM). Results indicate that TR significantly enhances PEOU, while RI negatively impacts it, aligning with prior studies linking trust and usability. Higher PR also boosts PEOU, suggesting that clearer data ownership rights could improve AI adoption. These findings highlight the importance of transparent systems and defined data ownership to foster AI integration in agriculture

The packaging industry is evolving through the integration of Smart Product Service Systems (SPSS) and Internet of Things (IoT) technologies, driven by regulatory demands and the shift towards Industry 5.0 principles. Despite advances in automation, the integration of SPSS remains challenging, especially in terms of system adaptability. This paper explores the differing approaches of machine builders and component manufacturers in SPSS development using Design Science Research (DSR). It highlights key differences in system integration, customization, and technology deployment. While machine builders focus on integrating complex systems, component manufacturers leverage advanced technologies. These insights contribute to the development of adaptive, human-centric manufacturing systems, aligning with Industry 5.0 and the Cyber Resilience Act (CRA).

The significance of digital technologies in the context of digitizing production processes, such as Artificial Intelligence (AI) and Digital Twins, is on the rise. A promising avenue of research is the optimization of digital twins through Reinforcement Learning (RL). This necessitates a simulation environment that can be integrated with RL. One is introduced in this paper as the Digital Model Playground (DMPG). The paper outlines the implementation of the DMPG, followed by demonstrating its application in optimizing production scheduling through RL within a sample process. Although there is potential for further development, the DMPG already enables the modeling and optimization of production processes using RL and is comparable to commercial discrete event simulation software regarding the simulation-speed. Furthermore, it is highly flexible and adaptable, as shown by two projects, which distribute the DMPG to a high-performance cluster or generate 2D/3D-Visualization of the simulation model with Unreal. This establishes the DMPG as a valuable tool for advancing the digital transformation of manufacturing systems, affirming its potential impact on the future of production optimization. Currently, planned extensions include the integration of more optimization algorithms and Process Mining techniques, to further enhance the usability of the framework.

In this research work, the calculation of carbon dioxide emissions for trailer traffic based on sensor data is described. The data consist of trailer type, load, trip and route parameters. While the individual trailer parameters can be derived from the order management, the trip and route parameters are collected by frequently data. With this, the transport carbon footprint is calculated based on DIN EN 16258 by means of the ProBas database. The distributions of carbon dioxide emissions in trailer traffic are analysed and discussed. The results of the case study indicate that sensor-based modelling can be a useful tool for an improved transparency and estimation of carbon dioxide emissions.