Serious Games Application for Memory Training Using Egocentric Images
Gabriel Oliveira-Barra, Marc Bola\~nos, Estefania Talavera and, Adri\'an Due\~nas, Olga Gelonch, Maite Garolera

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.
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Taxonomy
TopicsMultimodal Machine Learning Applications · Human Pose and Action Recognition · Advanced Image and Video Retrieval Techniques
