基于FCM聚类和卷积神经网络的跌倒识别算法

Fall Recognition Algorithm Based on FCM Clustering and Convolutional Neural Network

为了提高传统跌倒检测系统的识别准确度和运算速度,减小误报率和漏报率,本文提出了一种基于模糊C均值(Fuzzy C⁃means,FCM)聚类算法和卷积神经网络算法的实时跌倒检测算法。该算法以深度视觉传感器为数据获取源,提取聚类中心点速度、高度、加速度以及夹角为跌倒识别特征向量,采用阈值分析和机器算法相结合的方式实现人体跌倒识别。实验表明,该算法的识别精度达到99%,运算速度为0.178 s,相对于传统算法具有更高的识别精度和运算速度。

In order to improve the recognition accuracy and operation speed of the traditional fall detection system and reduce the false alarm rate and the missing alarm rate,a real-time fall detection algorithm based on fuzzy C-means(FCM)clustering algorithm and convolutional neural network algorithm is proposed.The algorithm takes the depth vision sensor as the data acquisition source,extracts the velocity,the height,the acceleration,and the angle of the cluster center point as the fall recognition feature vector,and uses the combination of threshold analysis and machine algorithm to realize human fall recognition.The experimental results show that the recognition accuracy of the algorithm reaches 99% and the operation speed is 0.178 s,which is higher than those of the traditional algorithm.