基于半监督学习的跌倒检测系统设计

针对老人跌倒时的复杂运动情况，进行跌倒标注的较难实现，提出了基于Tri-training半监督算法的跌倒检测系统。本系统使用3D加速度传感器采集运动加速度数据，然后对数据进行特征提取与部分样本标注，使用Tri-training算法训练分类器，最后使用训练好的分类器进行跌倒识别。具体的数据采集传感器设计为可穿戴式设备，服务器端使用Java编写了一个服务器的程序实现对数据的分析与处理。实验结果表明：该方法使用了大量无标签数据的信息，有效提高了跌倒识别的准确率。实验结果表明：本系统能够满足老年人在日常生活中的需求，对于一些意外跌倒能够给予及时的检测与报警。

Design of fall detection system based on semi-supervised learning

Aiming at problem that falling down movement of the elderly is very complex and falling down label is difficult to achieve,a stumbling and falling system based on Tri-training semi-supervised algorithm is proposed. The system uses the 3D acceleration sensor to collect movement accelerating data,and extraction of feature is done on the data and partial sample is labeled. Tri-training algorithm is used to train the classifier in the next step. Trained classifier is served to recognize stumbling and falling. The sensor of data acquisition is designed as a wearable device in particular and the server uses Java to write a program for data processing and analyzing. The test results show that the proposed method can effectively improve the accuracy of the recognition by applying a large number of unlabeled data. The experimental results demonstrate that the system can meet the needs of the elderly in their daily lives,and some unexpected falls are able to give timely detection and alarm.