基于加速度传感器的人体跌倒检测方法

针对人体跌倒检测阈值算法在由于阈值设定不当而引起的检测精度下降问题,采用支持向量机方法决定跌倒检测的阈值大小。从加速度传感器中获取人体运动信号,提取合加速度以及倾角作为分类特征,根据人体在跌倒时经过的失重、撞击地面和平稳三个阶段,建立基于阈值的跌倒检测模型。采用所建立的跌倒检测模型,分别用支持向量机方法以及人工方法设定阈值,仿真结果显示采用支持向量机设定阈值的检测效果优于对比算法,结果表明本文方法能有效识别跌倒。

A human fall detection algorithm based on acceleration sensor

Aiming at the accuracy degradation problem caused by improper threshold setting for human fall detection threshold algorithms, we introduce the support vector machine to select thresholds. We obtain human motion information from an acceleration sensor, and extract acceleration and angle as classification features. We propose a fall detection model based on thresholds according to the three stages of human fall: free fall, hitting the ground and stationary. The fall detection model sets thresholds by the support vector machine and manual method respectively, and simulation results show that compared with the manual-threshold-setting method, the accuracy of the proposal is higher, which proves the effectiveness of the proposed method.