基于D-S证据理论的人体跌倒检测方法

A human fall detection method based on D-S evidence theory

针对人体跌倒检测算法存在错误否定率高的问题,研究了一种基于D-S证据理论的人体跌倒检测算法。采用智能手机内置的加速度传感器和陀螺仪传感器获得人体手臂运动的三维方向的运动数据,采用三阶滑动平均滤波器对获得的两个传感器的三维原始数据进行预处理;从三维预处理后的数据中提取运动幅度、倾斜程度以及旋转程度三种特征;采用动态时间规整方法分别依据三种特征进行局部检测,局部检测结果作为证据被D-S证据理论组合规则所采用以得到最终融合的全局检测结果,其中各证据被证据权修正以避免证据冲突问题。实验结果显示,本文算法准确度高于对比方法,能有效提高检测性能。

Abstract：To solve the problem of high false negative rate in human fall detection, a detection algorithm based on D S evidence theory is studied. Acceleration sensors and gyro sensors are built into a sm artphone to obtain the three dimensional motion data of the human body＇s arm movement. A three step moving average filter is used to preprocess the three dimensional raw data obtained from the two types of sensors. Then, the detection features consisting of movement range, inclination degree and rotation degree are extracted from the three dimensional preprocessed data. The dynamic time warping method is used for local detection according to the three features. The local decision results act as evidences to be fused by the combinational rule of D S evidence theory to get the final global detection result, in which each evidence is modified by the weight of evidence to avoid the evidence conflict problem. Experimental results show that the accuracy of the proposed algorithm is higher than that of other algorithms, which can effectively improve the detection performance.