# Analysis of Facial Cues for Cognitive Decline Detection Using In-the-Wild Data
## 5 Discussion
In contrast, Ref. [35] found that the presence of the head turn cue indicates CI whether the participant attended with a partner or came alone. The findings of this research, while preliminary, seem to be consistent with other research, which found that increases in EBR and head turn or movements can indicate a higher risk of progression to AD [5; 38]. It is also assumed that sex and age may play a vital role in the HTR. In the IVA\({}_{18}\) data, all of the female participants came with a partner regardless of their diagnostic class, whilst only two males from the ND class came with partners. Moreover, in the IVA\({}_{34}\) data, all the female participants who came with partners have health conditions, but only two males from the MCI and HC classes came with a partner. Ref. [35] investigated the severity and the incidence of the head turn cue on 125 patients by observing whether the patients showed a head turn cue during a cognitive test and found that the females tended to bring a partner. Their findings showed that women find it easier to depend on someone else when they face difficulties, whereas men feel obligated to deal with difficulties without help. Previous work compared men and women with CI in terms of the prevalence of behavioral symptoms and found that 'help seeking' and depression are more frequent in women [39]. However, men showed more regressive and aggressive behaviours than women. These findings contradict those of previous work that found partner presence to be independent of sex [36; 40] but dependent on age [22]. Moreover, [PERSON]. [40] reported that head turn indicates a CI regardless of sex or age. However, there is no information on whether these factors are affected by other external factors such as culture. To fully assess the clinical applicability and generalizability of the proposed system, future work should include validation on larger and more diverse multi-center datasets. Moreover, benchmarking the method against state-of-the-art deep learning models--which have shown strong potential in similar applications--will be essential for evaluating its scalability and practical relevance in real-world clinical settings. Although deep learning approaches typically require larger datasets, recent developments in video-based cognitive assessment, using architectures such as convolutional neural networks (CNNs) and transformers, show considerable promise and should be explored as complementary or  
Figure 13: The confusion matrices for the different classification tasks using the interquartile range (IQR) approach to detect the upper boundary (UB) with a range of factors applied to 70 features or thresholds. Darker colors indicate higher true predicted values, while lighter colors indicate lower predictions. (rows: true labels; columns: classified labels). alternative methods. Further work is also needed to systematically quantify how environmental factors such as lighting variability, camera angle, or background clutter affect classification accuracy. While our method is designed for naturalistic \"in-the-wild\" settings--capturing real-world variation in participants' behavior and environment--this very complexity introduces challenges that can negatively impact classification performance. The variability in lighting, camera positioning, background stimuli, and participant compliance may introduce noise or inconsistencies in the extracted features. This may partly explain why the classification accuracy is more modest compared to studies using controlled lab environments. In this context, it is worth noting that participants in our study completed tasks in varied home environments, introducing potential differences in lighting, background, and incidental stimuli that could influence feature extraction. We acknowledge that these factors may limit the reliability and consistency of certain features, such as head movement or eye behavior, under uncontrolled conditions.