The assessment of muscle oxygenation by non-invasive near-infrared spectroscopy generally assumes a homogeneous medium, and this is flawed for large adipose tissue layers underneath the skin. Here we summarize the influence of the adipose tissue thickness on the oxygenation data, show that the adipose layer can be measured by NIRS and indicate a possible correction algorithm. Spectroscopic evidence suggests the usefulness of this algorithm, however, not in all subjects.
Artificial intelligence can be used to realise new types of protective devices and assistance systems, so their importance for occupational safety and health is continuously increasing. However, established risk mitigation measures in software development are only partially suitable for applications in AI systems, which only create new sources of risk. Risk management for systems that for systems using AI must therefore be adapted to the new problems. This work objects to contribute hereto by identifying relevant sources of risk for AI systems. For this purpose, the differences between AI systems, especially those based on modern machine learning methods, and classical software were analysed, and the current research fields of trustworthy AI were evaluated. On this basis, a taxonomy could be created that provides an overview of various AI-specific sources of risk. These new sources of risk should be taken into account in the overall risk assessment of a system based on AI technologies, examined for their criticality and managed accordingly at an early stage to prevent a later system failure.
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