Serious Games Application for Memory Training Using Egocentric Images
This work addresses memory training for patients with mild cognitive impairment, but it is incremental as it applies existing serious games concepts to a new image selection method.
The paper tackles the problem of using lifelogging images for memory training in mild cognitive impairment patients by introducing a computer vision technique to classify egocentric images for use in serious games, achieving a 79% F1-score on a dataset of 10,997 images from 7 users.
Mild cognitive impairment is the early stage of several neurodegenerative diseases, such as Alzheimer's. In this work, we address the use of lifelogging as a tool to obtain pictures from a patient's daily life from an egocentric point of view. We propose to use them in combination with serious games as a way to provide a non-pharmacological treatment to improve their quality of life. To do so, we introduce a novel computer vision technique that classifies rich and non rich egocentric images and uses them in serious games. We present results over a dataset composed by 10,997 images, recorded by 7 different users, achieving 79% of F1-score. Our model presents the first method used for automatic egocentric images selection applicable to serious games.