[1]牛德姣,刘亚文,蔡涛,等.基于递归神经网络的跌倒检测系统[J].智能系统学报,2018,13(3):380-387.[doi:10.11992/tis.201710013]
NIU Dejiao,LIU Yawen,CAI Tao,et al.Fall detection system based on recurrent neural network[J].CAAI Transactions on Intelligent Systems,2018,13(3):380-387.[doi:10.11992/tis.201710013]
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NIU Dejiao,LIU Yawen,CAI Tao,et al.Fall detection system based on recurrent neural network[J].CAAI Transactions on Intelligent Systems,2018,13(3):380-387.[doi:10.11992/tis.201710013]
基于递归神经网络的跌倒检测系统
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
13
期数:
2018年第3期
页码:
380-387
栏目:
学术论文—智能系统
出版日期:
2018-05-05
- Title:
- Fall detection system based on recurrent neural network
- Keywords:
- fall detection; near fall detection; sensor data; recurrent neural network; big data; fall detection algorithm; training algorithm; RNNFD
- 分类号:
- TP391
- 摘要:
- 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。
- Abstract:
- 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.
- 参考文献/References:
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备注/Memo
收稿日期:2017-10-17。
基金项目:江苏省科技厅重点研发计划产业前瞻与共性关键技术项目（BE2015137）；江苏省自然科学基金项目（BK20140570）；中国博士后基金项目（2016M601737）.
作者简介:牛德姣,女,1978年生,副教授,博士,主要研究方向为神经网络、新型非易失存储器。发表SCI和EI检索论文10余篇;刘亚文,女,1994年生,硕士研究生,主要研究方向为神经网络、大数据计算;蔡涛,男,1976年生,副教授,博士,CCF会员,主要研究方向为面向大数据人工智能和新型非易失存储器。发表SCI和EI检索论文30余篇。
通讯作者:牛德姣.E-mail:djniu@ujs.edu.cn.
更新日期/Last Update:
2018-06-25