## 1 Introduction  
Industrial maintenance and assembly tasks are extremely challenging operations that require physical abilities such as fine motor control and bimanual coordination. These tasks can overwhelm employees who must perform many actions requiring a lot of effort in a short period [1]. AR systems are now extensively used as an effective guiding tool for extremely challenging maintenance tasks [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. Compared to traditional assistance techniques for delivering instructions and job-relevant information, AR tools have been found to be more engaging, leading to increased productivity and fulfilling a quality control function [13; 14; 15; 16; 17; 18; 19]. Furthermore, for more challenging tasks requiring strategic planning and intensive problem solving, AR methods have shown considerable benefits over conventional methods by enhancing the cognitive comprehension of tasks [6]. Previous studies have primarily focused on the efficacy of AR in terms of task completion time, success rates, and various subjective measures [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. It is, however, uncertain whether using AR instruction can lead to adverse effects such as motion sickness, increased mental workload, increased physical stress, and visual fatigue [33; 34; 35; 36; 37]. As the AR industry grows, it is essential to address potential safety concerns like physical stress associated with AR instruction. Physical stress can be defined as a complex state that can influence an individual's ability to remain alert and attentive [38] depending on task characteristics, conditions, and the individual. In the field of human factors research, one area of special importance is measuring physiological stress utilizing physiological indicators. It is, therefore, quite essential that the measurement methods used are accurate. The most frequent physiological method used for measuring physical stress is heart rate variability (HRV), which is extracted from an electrocardiogram (ECG) [39; 40; 41]. An ECG represents the graphical record of an individual's heart's electrical activity, produced by the depolarization and repolarization of the atria and ventricles [42]. HRV is the variation in the time intervals between consecutive heartbeats [43] and can be used as a sensitive diagnostic technique for monitoring the activity of the autonomic nervous system (ANS) in stressful situations [16; 44]. The central nervous system's autonomic branches affect the electrical activity of the heart through the sympathetic (SNS) and parasympathetic (PNS) divisions of the ANS, affecting heart rate (HR) and beat-to-beat rhythm. PNS activity promotes relaxation and regeneration, and SNS activity mobilizes the body for activities such as the \"fight or flight\" response [45; 46]. The ANS plays a crucial role in stress and workload [37], making it an intriguing source of information to investigate. Signal fluctuations in the heart's rhythm can be sent to the brain via the vagal nerve terminals, leading to increased emotional stress and a reduced cognitive capacity for processing visual information [44; 47; 48; 49]. It has been found that the cognitive and neurological functions of humans can influence their cardiac rhythm [50]. Conversely, the sensory input of the heart can also affect performance, cognitive functions, and working memory [47]. Previous studies have used HRV indicators to examine the physical stress associated with stereoscopic displays [40; 44; 49; 51; 52]. For instance, [PERSON] et al. [16] objectively evaluated user strain during picking tasks in an industrial warehouse while using either an AR system or a simple paper list instruction, with measurements based on HRV. Their results showed that long-term use (two hours) of the AR system did not significantly increase overall strain. The variation in stress induced by the AR system was similar to that produced by the paper list method. [PERSON] et al.