## 1 Introduction
Each category utilizes a unique approach to evaluate mental workload. Physiological measures, such as heart rate variability (HRV) and electrodermal activity (EDA), provide objective data on the operator's arousal and stress levels that can be used to infer mental workload. On the other hand, subjective measurements, such as the National Aeronautics and Space Administration Task Load Index (NASA-TLX), offer self-reported assessments of workload across various dimensions, including mental, physical, and temporal demands (as proposed by [15]). Performance methods, in turn, enable the evaluation of human mental and psychomotor performance under specific work conditions, helping to assess any performance decrements or variations due to mental workload effects. Lastly, job and task analysis methods examine task elements, physical and psychosocial work conditions, environmental factors, and the organization of the work process as potential sourcesof mental workload [16]. Among mental workload assessment methods, physiological metrics have gained popularity in the field of human factors research as a means of measuring mental workload levels, along with the importance and widespread use of subjective measures, job and task analysis, and performance assessments [17]. Physiological metrics offer valuable insights into how the human body responds to stimuli like work tasks. One benefit of psychological metrics is that they continuously and unbrokenly record data in real time [18]. However, interpreting these data is often challenging due to individual uniqueness. To address this, combining data with context or utilizing artificial intelligence analysis becomes necessary. Cardiac activity was handled by [19; 20] to study the dynamics of mental workload. Exploiting brain activity, ref. [21] studied the subtleties of mental workload. In a different direction, ref. [22] focused on eye-tracking technology and investigated its uses to comprehend mental workload during HRC. Additionally, ref. [23] investigated the cognitive components of HRC using pupillometry. Significantly, refs. [4; 24] adopted an all-encompassing strategy in their investigations, incorporating both cardiac and electrodermal activity-based sensors. Parallel to this, ref. [25] broadened the scope of their research by including sensors for cardiac, respiratory, and brain activity. The result was the creation of an adaptive human sensor framework specifically designed to improve human-robot collaboration. In an analogous setting, ref. [26] concentrated their research on body temperature, electrodermal activity, and cardiac activity, providing more insights into the dynamic, complex nature of physiological reactions in many contexts of HRC. However, a consequence of the use of different physiological metrics to assess workload--both in isolation and in combination--in HRC is that it can be easy to lose track of which methodologies have been employed to measure different parameters. Thus, in this systematic review, we analyzed the different approaches used in the assessment of mental workload in HRC settings. We aim to provide a detailed review of methods and methodologies used to operationalize mental workload and potential applications for researchers and professionals. To achieve this, this review explores four key research questions:  
* RQ1: What industrial tasks are employed to assess mental workload in HRC scenarios? * RQ2: How are subjective and performance-based measures integrated with physiological data to assess mental workload in HRC? * RQ3: What are the main physiological measures used to operationalize mental workload in HRC scenarios? * RQ4: What are the challenges and limitations associated with using physiological measures to assess mental workload in HRC? Several systematic reviews have explored the application of physiological sensors across various domains. Understanding and choosing suitable physiological metrics for mental workload (MWL) assessment in a variety of human-machine systems was presented by [27].