## 4 Discussion
### Physical Stress
These outcomes align with previously reported findings [98; 110]. Overall, HRV indices are sensitive indicators of physical stress. The fluctuations in HRV delineate the duration of specific information extractions during engagement in cognitively demanding tasks. This phenomenon was further supported by an interaction effect seen with the instructional approach, which can be attributed to contrasting inclinations in the processing of difficult tasks. These findings revealed that using AR instruction significantly reduced the physical stress for the complex task, implying that AR instruction is better than paper instruction for complex tasks. In addition, the physical workload of the AR users decreased while participants performed the maintenance tasks, whether simple or complex. As the two tasks were performed in an AR setting, the HRV metrics remained approximately constant. In contrast, the HRV metrics increased when performing the complex task in a paper-based setting. This means that the instruction methods and thus the workloads increased with complex tasks but remained relatively constant for simple tasks. It is likely that the individuals had developed a level of familiarity with the nature of simple tasks and the corresponding processes of decision making, searching, and maintenance. This familiarity served to diminish the necessity for the further extraction of information. The AR system independently reduced the physical workload. The elevation in physical load resulted in a disruption of the homeostatic equilibrium, leading to subsequent allostatic adaptations. These adaptations, in turn, influenced the temporal spacing between R-peaks, therefore affecting HR and HRV parameters. HRV is recognized not only as a measure of ANS equilibrium but also as an indicator of central-peripheral neural feedback and the integration between the central nervous system (CNS) and the ANS [47,111]. There are some limitations to the current study that warrant mention. This research did not track how the participant's upper body moved. Monitoring kinematic variances between individuals may help in understanding the variety of stiffness responses to complex tasks and different instructional approaches. In addition, all participants in this study were male. Future work should consider incorporating women and other physiological signals, such as upper arm muscular activity, into the evaluation. Furthermore, the age range, education level, and formation of the participants in this study were quite limited. Future investigations could benefit from exploring the implications of our findings for broader age groups and different participants with different education levels and formations. Also, while the current study provides insights into the effects of AR instructions, it is limited to short-term use. The long-term implications of daily, extended use of AR in occupational settings remain to be explored. Another limitation of this study is the relative inexperience of the target participants. While this choice was deliberate to simulate a training scenario for new workers or for introducing new complex tasks, it may not fully represent the experiences of seasoned operators. To better generalize our findings, future research should include experienced participants. This will allow for a more comprehensive understanding of AR technology's utility across different skill levels, particularly for training on new or complex tasks within industrial settings. A notable limitation arose from the specific selection and use of interface types (visual and auditory), technological tools (such as paper, HMD, laptop, and HHD), and research design choices (including between, within, or mixed designs). In this study, a mixed experimental design was implemented, incorporating one between-subject variable (instruction methods) and one within-subject variable (task complexity). Task complexity was counterbalanced across participants to avoid order effects.