Open Access

As part of the “Perspectives on labour research Lusatia” (PAL) project, five chairs at Zwickau University of Applied Sciences are working with industrial partners to develop low-threshold methods and tools for simplified screening of work characteristics. Traditionally, the assessment of work systems requires extensive specialist knowledge in the areas of occupational safety, ergonomics and operational design. Creating a hazard and stress register as a basis for work system design is therefore time-consuming and most of the times cost-intensive, which leads to a wait-and-see attitude, especially among small and medium-sized companies. However, innovations in the field of work analysis are now enabling the use and integration of smart, digital assistance systems, such as smartwatches or fitness trackers, combined with portable, intelligent environmental measurement technology. This combination allows an autonomous, in-house assessment of the stresses occurring without the need for external specialists and expensive equipment. The protection of personal rights is guaranteed by anonymized and pseudonymized data transmission. The departmental or process-specific evaluation of the collected data using machine learning creates an indicative stress assessment that enables work to be organized in line with all requirements. The resulting rough classification of key areas for action serves to define priorities for action and supports targeted decision-making processes for further measures, in which experts are involved on a selective basis. As a result, companies can carry out a focus-oriented and therefore economically sensible optimization of work design. Of particular importance, however, are the expected positive effects on employees, such as increasing motivation as well as higher job satisfaction.

Introduction: The Apple Watch valuably records event-based electrocardiograms (iECG) in children, as shown in recent studies by Paech et al. In contrast to adults, though, the automatic heart rhythm classification of the Apple Watch did not provide satisfactory results in children. Therefore, ECG analysis is limited to interpretation by a pediatric cardiologist. To surmount this difficulty, an artificial intelligence (AI) based algorithm for the automatic interpretation of pediatric Apple Watch iECGs was developed in this study. Methods: A first AI-based algorithm was designed and trained based on prerecorded and manually classified i.e., labeled iECGs. Afterward the algorithm was evaluated in a prospectively recruited cohort of children at the Leipzig Heart Center. iECG evaluation by the algorithm was compared to the 12-lead-ECG evaluation by a pediatric cardiologist (gold standard). The outcomes were then used to calculate the sensitivity and specificity of the Apple Software and the self-developed AI. Results: The main features of the newly developed AI algorithm and the rapid development cycle are presented. Forty-eight pediatric patients were enrolled in this study. The AI reached a specificity of 96.7% and a sensitivity of 66.7% for classifying a normal sinus rhythm. Conclusion: The current study presents a first AI-based algorithm for the automatic heart rhythm classification of pediatric iECGs, and therefore provides the basis for further development of the AI-based iECG analysis in children as soon as more training data are available. More training in the AI algorithm is inevitable to enable the AI-based iECG analysis to work as a medical tool in complex patients.