基于递归神经网络的跌倒检测系统

针对现有跌倒检测方法存在适应性差和功能较单一等问题，引入递归神经网络，通过发掘位置传感器数据之间的内在联系提高检测跌倒行为的效果。首先，设计了传感器、训练与检测输入数据的序列化表示方法，为发掘其中与跌倒和接近跌倒行为相关的内在关联提供了基础；接着，给出了用于跌倒检测的RNN训练算法以及基于RNN的跌倒检测算法，将跌倒检测转换为输入序列的分类问题；最后，在前期实现的基于分布式神经元大规模RNN系统的基础上，在Spark平台上实现了基于RNN的跌倒检测系统，使用Fall_adl_data数据集进行了测试与分析，验证了其能有效提高跌倒检测的准确率和召回率，F值相比现有跌倒检测系统提高12%和7%，同时能有效检测出接近跌倒的行为，有助于及时采取保护措施减少伤害。
The existing methods of fall detection have poor adaptability and limited functions. In this paper, a recurrent neural network based fall detection system is introduced to improve the performance of fall detection and to make it able to identify more dangerous near-falls by exploring the relationship of the position sensor data. Firstly, a serialization representation method on position sensor data, training and test data is designed as the basis for intrinsic relationship exploration. Then, the training algorithm for RNN based fall detection is proposed, where the fall detection is transformed into a classification problem of the input sequence. Finally, using the large-scale RNN system based on distributed neurons, the fall detection system is implemented on the Spark platform. Evaluations are carried out on Fall_adl_data. The experimental results prove that the proposed system can improve the precision and recall of fall detection effectively. Compared with the existing fall detection systems, F-measure has improved by 12% and 7%, respectively. Moreover, the system is also able to detect the near-fall behavior effectively which helps provide timely protective measures to reduce the damage caused by falls