Serious Games Application for Memory Training Using Egocentric Images

TL;DR

This paper introduces a novel computer vision method that classifies egocentric images to be used in serious games, aiming to provide a non-pharmacological treatment for memory impairment in neurodegenerative diseases.

Contribution

It presents the first automatic egocentric image selection technique for serious games targeting cognitive impairment treatment.

Findings

Achieved 79% F1-score on a dataset of 10,997 images.

Developed a new method for classifying rich and non-rich egocentric images.

Demonstrated potential for non-pharmacological memory training applications.

Abstract

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.