## 5 Conclusions  
The results of this systematic literature review highlight the potential of physiological metrics for monitoring mental workload. However, this study also identifies significant challenges in applying them to collaborative robotics, worsened by the lack of universal consensus on its conceptualization and assessment. Diverse definitions of workload and a lack of standardization in monitoring methodologies further complicate the issue. While various metrics demonstrate the capability to measure mental workload, differences arise in the approach to gathering physiological variables. Some studies focus on a single metric, while others adopt a multimodal approach for a comprehensive understanding of mental workload in diverse collaborative scenarios. The fact remains that no single metric currently exists that is exempt from limitations or being affected by outside noise, such as humidity, temperature, or a subject's medication. However, as technology progresses, one can expect the impact of these limitations to be decreased or eliminated outright. In the meanwhile, authors working with mental workload assessments have access to various tools to that end, allowing them to adapt to different use cases. For example, while current instruments to measure brain activity, such as the EEG and fNIRS, are prone to noise due to the subject's motion (e.g., walking around the room versus being stationary at a desk), wearable instruments that assess heart rate data while a subject is moving, such as smartwatches and smart rings, can be easily employed instead. While the heart rate measurement from these may be less reliable than other, more bulky instruments, they can nevertheless be good enough alternatives for situations where such instruments are not applicable or cumbersome, and the same can be said for other combinations of instruments as well. As the field progresses, collaborative efforts among researchers and practitioners are crucial to defining robust methodologies, validating models across varied human-robot collaboration scenarios, and establishing guidelines for their implementation. This concerted approach will facilitate the development of machine learning models that not only enhance the accuracy of mental workload assessments but also ensure their applicability and reliability in real-world collaborative robotic environments. In the context of Industry 5.0, the utilization of various physiological variables as inputs for machine learning models holds promise for powering intelligent algorithms that can decode human movement intentions and adapt robot behavior proactively. However, challenges persist in understanding the relationships among physiological variables and in effectively employing machine learning models to accurately assess mental workload. These challenges can be addressed by applying explainable artificial intelligence (XAI) to enable the interpretation and trust of the results generated by machine learning algorithms [81]. Further research is needed to evaluate the generalizability of the models across different tasks and to ensure high accuracy for industrial deployment. Addressing subject-specific factors, electrode placement variability, and changing environmental conditions in industrial settings will be essential to improve the practical implementation of cognitive workload classification systems. In conclusion, while a universally validated method is not yet established, the foundation for future research lies in systematic description, ensuring the dependability of forthcoming models. Despite challenges, the integration of diverse methodologies holds significant potential in assessing mental workload and shaping the future of human-robot collaboration for enhanced safety, health, well-being, and productivity. This synthesis of findings emphasizes the need for continued exploration and refinement, paving the way for a more robust understanding and application of workload assessment methodologies in diverse contexts.