Development of childhood fall motion database and browser based on behavior measurements

Abstract

This paper describes the development of a fall motion database and a browser designed to facilitate investigations into fall-related injury risk. First, child-related daily activities were collected at a “sensor home”, which is a model of a normal living environment equipped with an embedded video-surveillance system and within which child test subjects were equipped with wearable acceleration-gyro sensors. As of this report, measurements have been conducted for 19 children (months age: mean=23.8, standard deviation=10.5), and data has been obtained on 105 fall incidents. During our research, falls were detected from the accumulated sensor data using a detection algorithm developed by the authors, and then video clips of detected falls were extracted from the recorded video streams automatically. The extracted video clips were then used for fall motion analysis. A computer vision (CV) algorithm, which was developed to automate fall motion analysis, facilitates accumulation of fall motion data into the abovementioned database, and the associated database browser allows users to perform conditional searches of fall data by inputting search conditions, such as child attributes and specific fall situations. Before this study, there was no database which contains child's actual fall motion data, and it has the potential to facilitate injury risk reduction related to falls in daily living environments.