Evaluating Feasibility of Pose Detection with Image Rotation for Monitoring Elderly People at Home

This study aims to enhance human pose detection performance through image preprocessing. With the growing global elderly population, supporting in-home elderly individuals has become increasingly crucial, especially in Japan, where approximately 90% of the elderly live at home. In this study, abnormal behaviors in in-home elderly individuals were detected using high-precision pose detection technologies, such as the MoveNet model, based on machine learning. However, there was a discrepancy in pose detection accuracy between the standing and lying positions. Hence, preprocessing techniques such as image rotation, flipping, resizing, and noise reduction were applied, and their effects were analyzed in detail. As a result, an improvement in pose detection accuracy for most body parts, except for specific regions, was observed. By constructing an optimal preprocessing pipeline, it is expected to reduce false detection and missed detection, contributing to the practical application of pose detection technology.