Investigating Visual Features for Cognitive Impairment Detection Using In-the-wild Data

Abstract

Early detection of dementia has attracted much research interest due to its crucial role in helping people get suitable treatment or care. Video analysis may provide an effective approach for detection, with low cost and effort compared to current expensive and intensive clinical assessments. This paper investigates the use of a range of visual features - eye blink rate (EBR), head turn rate (HTR) and head movement statistical features (HMSF) - for identifying neurodegenerative disorder (ND), mild cognitive impairment (MCI) and functional memory disorder (FMD). These features are used in a noval multiple thresholds approach, which is applied to an in-the-wild video dataset which includes data recorded in a range of challenging environments. A combination of EBR and HTR gives 78 % accuracy in a three-way classification task (ND/MCI/FMD) and 83%, 83% and 92%, respectively, for the two-way classifications ND/MCI, ND/FMD and MCI/FMD. These results are comparable to related work that uses more features from different modalities. They also provide evidence to support the possibility of an in-the-home detection process for dementia or cognitive impairment.