Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 242-246.doi: 10.11896/JsJkx.191000077

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Fall Detection Algorithm Based on BP Neural Network

ZHOU Li-peng1, MENG Li-min1, ZHOU Lei1, JIANG Wei2 and DONG Jian-ping3   

  1. 1 College of Information Engineering,ZheJiang University of Technology,Hangzhou 310023,China
    2 College of Information Science and Technology,ZheJiang Shuren Unuversity,Hangzhou 310015,China
    3 Information Department,Taizhou Administrative College,Taizhou,ZheJiang 318000,China
  • Published:2020-07-07
  • About author:ZHOU Li-peng, born in 1994, postgra-duate.His main research interests include wireless communication signal processing and system design, machine learning, etc.
    MENG Li-min, born in 1963, Ph.D, professor, Ph.D supervisor.Her main research interests include wireless communication and network, intelligent information system, network management, multimedia digital communication and network, etc.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61871349) and Basic public welfare proJects in ZheJiang (LY18F010024,LQ19F010013).

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Abstract: Fall is a very serious problem for the elderly.Real-time detection of whether the elderly fall or not is of great significance to reduce the inJury caused by falling.Therefore,a fall detection algorithm based on BP neural network is proposed in this paper.The algorithm collects human motion data with a six-axis sensor (MPU6050) worn at the waist,and uses a simple statistical method to extract features from the data.The extracted features are used as input neurons of BP neural network,and Levenberg-Marquardt algorithm is used to train the neural network model,so that it can realize the function of fall detection.Experimental results show that the algorithm can recognize falls well and the accuracy can reach 99.55%.

Key words: BP neural network, Fall detection, Feature extraction, Pattern recognition, Wearable equipment

CLC Number: 

  • TP183
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