# Analysis of Facial Cues for Cognitive Decline Detection Using In-the-Wild Data
## 3 Methodology
### Data
#### 3.1.2 IVA\({}_{34}\) Data  
These data were collected in two different environments, a clinic and at home. People at home used laptops and smartphones to carry out the recording. There are 34 videos (19 female and 15 male) representing four groups: 5 participants with ND, 4 with MCI, 2 with FMD, and 23 HCs. The total duration of the videos is 538.59 min (mean = 9.79 min, SD = 5.54 min). Originally, more participants were recorded, but eight were excluded as face detection was difficult due to the following problems: room too dark; interruptions from the participant's partner; a participant's eyes not being visible to the camera; two participants wearing masks; the extreme angle of a participant from the smartphone camera; a participant continually looking and talking to the right at the person who was operating the laptop.  
As with the IVA\({}_{18}\) data, in the clinic recordings participants, were told that they could bring someone with them. A total of 4 out of the 13 female participants recorded in a clinic brought a caregiver or partner with them (3 ND and 1 MCI). Consequently, some videos contain four people, as mentioned in the previous section. In contrast, only 2 out of the 20 participants who made a home recording had a caregiver/partner with them during the session (1 MCI and 1 HC).  
The use of home environments for recording created many challenges. a participant could choose which room to sit in (e.g., office, living room, bedroom), the distance and the angle from the camera, whether the lights of the room were on or off, and whether to participate in the session during the day or at night time. Rooms chosen could have noisy backgrounds (including furniture such as an office table, or pictures of people, and various objects). In addition, different devices could be used for the recording, e.g., laptops and smartphones. laptops could be set at any angle with respect to a participant, and the participant could turn their face away from the camera at any time or move out of camera leaving only part of their face visible. Where a smartphone was used, the angle of the camera to the viewer could be continually changing. Smartphone recordings also meant participants generally held the phone and its camera closer to their face and at a lower angle than laptop users, making their head orientation different and their eyes look partially closed. In all cases, people wearing glasses, which was common for the age range captured, also caused challenges.  
Although clinic recordings and home recordings share several challenges, the challenge of people appearing with the participant in the camera view is more common in clinic recordings than in home recordings. In the clinic recordings, at least two people appear on  
Figure 1: A screen-shot that presents the IVA when it is in use [29].  
camera, as previously mentioned. Taking all of this into account, such data pose a challenge for video-based processing.  
In contrast to the IVA\({}_{18}\) data, the IVA\({}_{34}\) data include very few participants with health conditions. This makes them less suitable for use in experiments to distinguish between participants with ND, MCI, FMD, and HCs. Therefore, we combined the IVA\({}_{34}\) data with the IVA\({}_{18}\)data, which resulted in a larger more balanced dataset with 52 participants, which is referred to as IVA\({}_{52}\) in the rest of this work. Previous research using the IVA\({}_{52}\) dataset primarily focused on speech processing [29]. This study is the first to analyze the video recordings from this dataset for cognitive impairment detection.