## 3 Results
### Physiological Assessment of Mental Workload
#### 3.6.2 Cardiac Measures  
Cardiac measures, featured in 10 of the reviewed studies, also stand as a significant contributor, affirming the physiological responsiveness of the cardiovascular system as a valuable measure for workload assessment. Heart rate (HR), defined as the number of heartbeats per minute (bpm), has traditionally been the most used signal feature. However, findings on its sensitivity to workload variations in HRC remain mixed. Several studies reported no significant differences in HR across experimental conditions. Ref. [12] found that the average heart rate did not significantly differ between HRC and manual task scenarios. Similarly, ref. [40] observed no significant variations in HR, reinforcing the idea that HR alone may not always be a strong discriminator of cognitive load in collaborative environments. Ref. [50] further confirmed this, reporting no significant difference in subjective mental workload or HR between adaptive and standard speed conditions in an industrial HRC set-up. On the other hand, some studies have indicated a moderate correlation between HR and cognitive load. Ref. [34], in their study on classifying cognitive load in HRC using physiological feedback, identified HR as sensitive to changes in mental effort. Supporting this, ref. [51] found a moderate positive correlation between cognitive load and HR, suggesting a slight increase in physiological activation with higher mental workload in collaborative industrial systems. In contrast, heart rate variability (HRV), the fluctuation in the time intervals between contiguous heartbeats, has, in turn, been touted as the most promising indicator for describing workload [18]. Time- and frequency-domain and nonlinear analysis are thought of as the main divisions of cardiac measures analysis, reflecting distinct aspects of autonomic nervous system modulation. Within the time-domain analysis (Table 3), the most relevant HRV features include SDNN (standard deviation of all successive normal R-R intervals), RMSSD (root mean square of the successive R-R interval differences), PNN50 (proportion of beats with a successive R-R interval difference exceeding 50 ms), and HRVTi (heart rate variability time index). While HRVTi and SDNN are associated with general autonomic influence, PNN50 and RMSSD are more directly linked to parasympathetic (vagal) activity [62]. Despite HRV being widely used, its relationship with task complexity and robotic assistance remains unclear. Ref. [43] found that HRV, measured through RMSSD and SDNN, did not show significant variations between manual and collaborative conditions, suggesting that task modality alone may not be a determining factor for physiological workload variations. However, other studies have reported more specific trends. Ref. [4] found that RMSSD was significantly higher in the manual assembly condition at both mid-session and the end of the task, indicating lower physiological stress. Furthermore, RMSSD showed a general increase throughout the session, suggesting progressive adaptation to the task. Additional insights from ref. [8] suggest that RMSSD was consistently lower in the HRC setting, implying that the operator maintained a sustained level of attention, possibly due to the structured nature of robotic assistance. In contrast, higher RMSSD values in manual conditions may indicate moments of relaxation or cognitive disengagement, potentially reflecting a greater likelihood of distractions in non-automated environments. The distribution of absolute or relative spectral power into four frequency bands is estimated by frequency-domain features (Table 4) named ultra-low-frequency, very-low-frequency, low-frequency, and high-frequency bands [63]. The power spectral of each frequency component, as well as the total power spectral of all frequency bands, are thought to be useful features for measuring mental workload. Sympathetic activity is assumed to be associated with LF-relative features. It is believed that there is a correlation between parasympathetic activity and HF-related features.