Explaining the Unique Behavioral Characteristics of Elderly and Adults Based on Deep Learning

• Published in: Applied sciences Vol. 11; no. 22; p. 10979
• Main Authors: Byeon, Yeong-Hyeon, Kim, Dohyung, Lee, Jaeyeon, Kwak, Keun-Chang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2021
• Subjects: Adults; Aging; Artificial intelligence; Behavior; behavioral characteristics; Classification; convolutional neural network; Datasets; Deep learning; Geriatrics; grad-cam; Image classification; Life span; Neural networks; Older people; Population; Product development; Robots; Sensors; Sequences; skeleton; Young adults
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app112210979

In modern society, the population has been aging as the lifespan has increased owing to the advancement in medical technologies. This could pose a threat to the economic system and, in serious cases, to the ethics regarding the socially-weak elderly. An analysis of the behavioral characteristics of the elderly and young adults based on their physical conditions enables silver robots to provide customized services for the elderly to counter aging society problems, laying the groundwork for improving elderly welfare systems and automating elderly care systems. Accordingly, skeleton sequences modeling the changes of the human body are converted into pose evolution images (PEIs), and a convolutional neural network (CNN) is trained to classify the elderly and young adults for a single behavior. Then, a heatmap, which is a contributed portion of the inputs, is obtained using a gradient-weighted class activation map (Grad-CAM) for the classified results, and a skeleton-heatmap is obtained through a series of processes for the ease of analysis. Finally, the behavioral characteristics are derived through the difference matching analysis between the domains based on the skeleton-heatmap and RGB video matching analysis. In this study, we present the analysis of the behavioral characteristics of the elderly and young adults based on cognitive science using deep learning and discuss the examples of the analysis. Therefore, we have used the ETRI-Activity3D dataset, which is the largest of its kind among the datasets that have classified the behaviors of young adults and the elderly.