Department of Mechanical Engineering, Faculty of Science and Technology, Tokyo University of Science, 2641 Yamazaki, Noda-shi, Chiba 278-8510, Japan; Digital Human Research Center, National Institute of Advanced Industrial Science and Technology, 2-3-26 Aomi, Koto-ku, Tokyo 135-0064, Japan.
Accid Anal Prev. 2013 Oct;59:432-42. doi: 10.1016/j.aap.2013.06.015. Epub 2013 Jul 3.
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.
本文介绍了一种跌倒运动数据库和浏览器的开发，旨在促进对跌倒相关伤害风险的研究。首先，在“传感器之家”收集与儿童相关的日常活动，这是一个配备嵌入式视频监控系统的正常生活环境模型，其中为儿童测试对象配备了可穿戴加速度陀螺仪传感器。截至本报告，已对 19 名儿童（月龄：平均值=23.8，标准差=10.5）进行了测量，并获得了 105 次跌倒事件的数据。在我们的研究中，使用作者开发的检测算法从累积的传感器数据中检测跌倒事件，然后自动从记录的视频流中提取检测到的跌倒的视频片段。然后使用这些提取的视频片段进行跌倒运动分析。开发了一种计算机视觉（CV）算法，用于自动进行跌倒运动分析，将跌倒运动数据积累到上述数据库中，并且相关的数据库浏览器允许用户通过输入搜索条件（如儿童属性和特定跌倒情况）对跌倒数据进行有条件的搜索。在此研究之前，没有包含儿童实际跌倒运动数据的数据库，它有潜力促进日常生活环境中跌倒相关伤害风险的降低。
