[1]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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Fall detection system based on recurrent neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 13 Number of periods: 2018 3 Page number: 380-387 Column: 学术论文—智能系统 Public date: 2018-05-05
Title:
Fall detection system based on recurrent neural network
Author(s):
NIU Dejiao;  LIU Yawen;  CAI Tao;  PENG Changsheng;  ZHAN Yongzhao;  LIANG Jun
School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212001, China
Keywords:
fall detection; near fall detection; sensor data; recurrent neural network; big data; fall detection algorithm; training algorithm; RNNFD
CLC:
TP391
DOI:
10.11992/tis.201710013
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.
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