## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & **Type of Fatigue** & **Number of Subject** & **Fatigue Assessment** & **Best Result Achieved** \\ \hline [PERSON] et al. [14] & Driving fatigue & 200 facial images with & Threshold based on eye & 98.30\% \\  & & fatigue state & and mouth features & \\ \hline [PERSON] et al. [15] & Driving fatigue & 36 subjects (360 videos) & Facial landmark points and head posture data & 85\% \\ \hline [PERSON] et al. [16] & Driving fatigue & 36 subjects & PERCLOS (Percentage of & \\  & & & Eyelid Closure over the & 96.4\% \\  & & & Pupil) and voice & \\ \hline [PERSON] et al. [18] & Driving fatigue & 10 subjects & KSS (Karolinska & \\  & & & Sleepiness Scale) & NRMSE of 15.63\% \\ \hline [PERSON] et al. [20] & Driving fatigue & \begin{tabular}{c} Thousands of images of \\ sleepy and active faces \\ \end{tabular} & Expert assessment & 93.69\% \\ \hline [PERSON] et al. [21] & Driving fatigue & \begin{tabular}{c} 36 subjects (360 videos) + \\ 2423 images of eyes \\ \end{tabular} & \begin{tabular}{c} Facial landmark points \\ and head posture data \\ \end{tabular} & 93.3\% \\ \hline [PERSON] et al. [22] & Driving fatigue & \begin{tabular}{c} 110 subjects (120 videos) \\ \end{tabular} & Yawns & 90\% \\ \hline [PERSON] et al. [24] & Driving fatigue & 20 subjects & KSS (Karolinska & 91.67\% \\  & & Sleepiness Scale) & \\ \hline [PERSON] et al. [28] & Operator fatigue & 10 subjects & \
\begin{tabular}{c} Landolt rings test (mental \\ performance indicator) \\ \end{tabular} & 85\% \\ \hline [PERSON] et al. [29] & Operator fatigue & 15 subjects & NASA-TLX & 99.941\% \\ \hline [PERSON] et al. [30] & Operator fatigue & 16 subjects & NASA-TLX & 96.2\% \\ \hline [PERSON] et al. [31] & Operator fatigue & 12 subjects & NASA-TLX & 92\% \\ \hline Our approach & Operator fatigue & 10 subjects (817 videos) &
\begin{tabular}{c} Landolt rings test (mental \\ performance indicator) \\ \end{tabular} & 98.43\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with existing approaches for fatigue detection. fatigue assessment methodologies primarily address driving fatigue, often overlooking the fatigue associated with office work. Consequently, we consider our research to be novel and innovative, as it specifically targets this issue through objective measurement techniques, i.e., the mental performance assessment using the Landolt ring. This study faced certain limitations due its relatively small size. However, this problem cannot be solved by merging our dataset with the other datasets mentioned in Section 2.2, because we collected different fatigue indicators in our dataset, such as VAS-F, Landolt rings test, and CRT. Based on our experiments described in [28], we found that Landolt rings test is the most reasonable in the considered case of fatigue detection. As a result, our models have been trained for the fatigue level estimated by the Landolt rings test, which is absent in the datasets mentioned in Section 2.2; hence, it is not possible to extend our dataset with the datasets introduced in Section 2.2. Additionally, another limitation was including group of participants with limited age diversity and ethnic background. As people age, heart rate and respiratory rate remain relatively stable, but maximal heart rate decreases, blood pressure--particularly systolic--tends to rise due to stiffening arteries, and oxygen saturation may decline, especially in those with chronic conditions. However, we have not studied how the data change across different age groups and ethnicities. These factors may hinder the generalizability of the proposed approach. One potential constraint that could impede the generalizability of the trained model is the fact that fatigue detection is human-dependent. This factor could pose challenges in applying the proposed model to novel subjects. Moreover, there remains ambiguity in determining the fatigue threshold, as the VAS-F result is subjective. To address this, we opted for the mental performance indicator as a replacement for fatigue.