Fall Detection Based on Inverted-Pendulum Model as Training Data for Monitoring Elderly People Living Solitarily Using Depth Camera

This study aims to develop a monitoring system for elderly individuals living solitarily using time-series data generated via simulation as training data. In particular, we focus on classifying three types of motion: falling, static standing, and walking. First, we create a system that calculates body velocity and acceleration using a depth camera. Based on actual measurements of each motion, we identify their distinct characteristics. Subsequently, we implement an inverted-pendulum model, which is commonly used for human-motion analysis, in a dynamics simulator. Simulations of falling, static standing, and walking are conducted, which successfully generated time-series data closely resembling the actual measured motions. Finally, using the simulation-derived time-series data as training data, we perform a machine-learning-based classification of falling, static standing, and walking motions measured using Azure Kinect. Although some misclassifications occurred, the system accurately classified most of the motions.