Seok-Jun Bu  Kyoung-Won Park  Sung-Bae Cho

10.5626/JOK.2023.50.3.243

Life Logging depression prediction sensory processing Semantic Network graph convolution network

Depression in the elderly is a global problem that causes 300 million patients and 800,000 suicides every year, so it is critical to detect early daily activity patterns closely related to mobility. Although a graph-convolution neural network based on sensing logs has been promising, it is required to represent high-level behaviors extracted from complex sensing information sequences. In this paper, a semantic network that structuralizes the daily activity patterns of the elderly was constructed using additional domain knowledge, and a graph convolution model was proposed for complementary uses of low-level sensing log graphs. Cross-validation with 800 hours of data from 69 senior citizens provided by DNX, Inc. revealed improved prediction performance for the suggested strategy compared to the most recent deep learning model. In particular, the inference of a semantic network was justified by a graph convolution model by showing a performance improvement of 28.86% compared with the conventional model.