## 2 Materials and Methods
### Response Variables
#### 2.4.3 Physical Stress  
The HRV parameters of the ECG were used to assess user stress during the performance of the tasks under the different instruction methods. A one-channel MT-ECG-1 preamplifier (Mega Electronics Ltd., Kuopio, Finland) with three lead wires was used to record the ECG signals. Ag/AgCl solid, adhesive, pre-gelled electrodes were positioned on the participants' chests to reduce the occurrence of muscle contraction artifacts during the task [65] (see Figure 10). Mega Win 3.0.1 software (Mega Electronics Ltd., Kuopio, Finland) was used to acquire the ECG signals at a sampling rate of 1000 Hz with a resolution of 12 bits. Before any further processing, the quality of the signal was visually inspected. To decrease high-frequency noise, the ECG signals were preprocessed via a 40 Hz cutoff low-pass filter. In addition, a 0.3 Hz cutoff high-pass filter was implemented to attenuate baseline wander. ECG signals were processed using Kubios HRV Analysis  
Figure 10: ECG electrode placement. Software version 2.2 [63]. The processing workflow of the ECG data is outlined in Figure 11. The ECG processing started with the previously filtered signals and the detection of the QRS complexes(the depolarization of the ventricles). Time series analyses of HRV began with the elimination of artifacts caused by erroneous RR intervals (i.e., falsely detected QRS complexes) [59; 66]. Artifacts in recorded ECG signals can cause significant distortion to ECG results [59]. The important part of the analysis focused on achieving two types of parameters: time domain and frequency domain features. A useful reference about the interpretation, measurement, and medical use of HRV can be seen in [59; 67]. Artifact beats were filtered out from the recorded ECGs by deleting RR intervals that deviated by more than 25% from one another [66]. For the data length to stay constant (i.e., with the same number of beats), the removed RR intervals were substituted with values obtained by linear interpolation. Low-frequency disturbances in the baseline trend were eliminated using the smoothness prior approach with a Lambda of 1000 [68; 69]. Within the HRV time domain, three types of variables were analyzed in this study: statistical, geometrical, and nonlinear. For statistical parameters, the mean RR interval (m-RR, in ms), standard deviation of normal-to-normal RR intervals (SDRR), mean heart rate (m-HR, in BPM), standard deviation of heart rate (SDHR), root mean square of successive RR interval differences (RMSD, in ms), and percentage of RR intervals differing \(>\)50 ms from the preceding one (pNN50) were computed. Geometrical features involved calculations on the HRV triangular index and the triangle base used to represent the RR time series histogram (TINN). Nonlinear HRV features such as sample entropy (SaEn), SD1, SD2, and approximate entropy (ApEn) were also calculated. A frequency domain analysis was also performed on the ECG data using fast Fourier transformations (FFT). [70]. The non-equidistant beat-by-beat time series was converted into a new time series sampled at uniform intervals using a quadratic interpolation at 4 Hz [71; 72]. Subsequently, any possible trends in the signals were eliminated by detrending, and the variations in the signals were extracted by suppressing the mean [73]. Power spectra of the periodic oscillations were obtained using the [PERSON]'s periodogram function with a 256 s window and 50% overlap, allowing for a frequency resolution of 0.015 to 0.02 Hz [72]. Frequency domain parameters were computed by dividing the RR time series PSD into three frequency bands, with the spectral power being the area below the power spectral density (PSD) for each period. Frequencies below 0.04 Hz were considered very low frequency (VLF). The area under the PSD from 0.04 Hz to 0.15 Hz was computed to yield the low-frequency power (LF), and the same process from 0.15 Hz to 0.4 Hz yielded the high-frequency power (HF). The  
Figure 11: Steps used for processing the ECG signals to obtain HRV analysis.