基于离散特征的跌倒检测智能方法及应用

随着人口老龄化现象加剧,对老年人跌倒的检测与报警越来越重要。为提高跌倒检测的准确率,提出一种基于离散特征的跌倒检测智能方法。通过对人体运动数据的分析,提出7类人体运动特征;并建立了以BP神经网络为基础的跌倒检测模型,将提取的离散特征作为模型的输入,模型的输出作为跌倒检测结果;通过对模型的学习与训练后,实现跌倒检测。方法验证和产品应用结果表明:采用基于离散特征的跌倒检测智能方法能够有效地区分跌倒与非跌倒,提高了跌倒检测正确率,降低了误报率和漏报率。

Intelligent fall detection method based on discrete feature and its application

As the population aging is aggravated, the detection and alarm of falls in elderly people are becoming more and more important. In order to improve the accuracy of fall detection, an intelligent fall detection method based on discrete feature is proposed. Through analyzing the human motion data, seven kinds of discrete features of human motion are proposed. A fall detection model based on BP neural network is established. The extracted discrete features are used as the inputs of the fall detection model, and the output of the model is the result of fall detection. Through the learning and training of the model, fall detection is achieved. The method verification and product application results indicate that the intelligent fall detection method based on discrete features can effectively distinguish fall and non fall, improve the fall detection accuracy and reduce the false alarm rate and missing alarm rate.