Artificial vision system to detect the mood of an Alzheimer's patient

Abstract

Dementia is a brain disorder that affects older individuals in their ability to carry out their daily activities, such as in the case of neurological diseases. The main objective of this study is to automatically classify the mood of an Alzheimer's patient into one of the following categories: wandering, nervous, depressed, disoriented, bored or normal i.e. in Alzheimer's patients from videos obtained in nursing homes for the elderly in the canton of Ambato, Ecuador. We worked with a population of 39 people from both sexes who were diagnosed with Alzheimer's and whose ages ranged between 75 and 89 years of age. The methods used are pose detection, feature extraction, and pose classification. This was achieved with the usage of neural networks, the walk classifier, and the Levenshtein Distance metric. As a result, a sequence of moods is generated, which determine a relationship between the software and the human expert for the expected effect. It is concluded that artificial vision software allows us to recognize the mood states of the Alzheimer patients during pose changes over time.

Keywords: Classification, the Levenshtein Distance, Pose, Neural Network, Machine Vision.

DOI: 10.54941/ahfe1001445