| 一种人体跌倒检测方法 |
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| 引用本文： | 茅莉磊,高强.一种人体跌倒检测方法[J].计算机系统应用,2016,25(5):142-146. |
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| 作者姓名： | 茅莉磊 高强 |
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| 作者单位： | 苏州大学 机电工程学院, 苏州 215021,苏州大学 机电工程学院, 苏州 215021 |
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| 基金项目： | 国家自然科学基金(51475315) |
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| 摘 要： | 随着人口老龄化问题日趋严重,针对老年人容易跌倒的社会问题,进行跌倒检测方法的研究.采用基于穿戴式设备的跌倒检测方法,不同于绝大多数的跌倒事后检测方法,结合加速度特征和角度特征,采用支持向量机算法作为分类算法,进行人体跌倒的事前检测.通过实验发现,跌倒行为的检测率达到99.2%,日常活动行为的检测率达到96%,跌倒检测的平均前置时间为273ms.
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| 关 键 词： | 跌倒 加速度 角度 支持向量机 前置时间 |
| 收稿时间： | 9/4/2015 12:00:00 AM |
| 修稿时间： | 2015/10/19 0:00:00 |
Method of Human Fall Detection |
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| MAO Li-Lei and GAO Qiang.Method of Human Fall Detection[J].Computer Systems& Applications,2016,25(5):142-146. |
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| Authors: | MAO Li-Lei and GAO Qiang |
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| Affiliation: | College of Mechanical and Electrical Engineering, Soochow University, Suzhou 215021, China and College of Mechanical and Electrical Engineering, Soochow University, Suzhou 215021, China |
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| Abstract: | As the problem of population aging is becoming more and more serious, a method of human fall detection based on wearable device is proposed to solve the social problem that the elderly are prone to fall. Different from the majority of fall detection methods which detect fall events after falling to the ground, the features of acceleration and angle are considered and support vector machine (SVM) is used as the classification algorithm to detect fall events before falling to the ground. The experiment results show that the fall event is recognized with a 99.2% recognition rate and the recognition rate of the activity of daily living is 96%. The average lead-time is 273ms. |
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| Keywords: | fall acceleration angle SVM lead-time |
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