Journal on Communications ›› 2017, Vol. 38 ›› Issue (Z2): 122-128.doi: 10.11959/j.issn.1000-436x.2017262

• Papers • Previous Articles     Next Articles

Inception neural network for human activity recognition using wearable sensor

Duo CHAI1,2,Cheng XU1,2,Jie HE1,2,Shao-yang ZHANG1,2,Shi-hong DUAN1,2,Yue QI1,2()   

  1. 1 School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing 100083,China
    2 Beijing Key Laboratory of Knowledge Engineering for Materials Science,Beijing 100083,China
  • Online:2017-11-01 Published:2018-06-07
  • Supported by:
    The National Key R&D Program of China(2016YFC0901303);The National Natural Science Foundation of China(61671056);The National Natural Science Foundation of China(61302065);The National Natural Science Foundation of China(61304257);The National Natural Science Foundation of China(61402033);Beijing Natural Science Foundation Project(4152036);Tianjin Special Program for Science and Technology(16ZXCXSF00150)

Abstract:

The experience from computer vision was learned,an innovative neural network model called InnoHAR (inception neural network for human activity recognition) based on the inception neural network and recurrent neural network was put forward,which started from an end-to-end multi-channel sensor waveform data,followed by the 1×1 convolution for better combination of the multi-channel data,and the various scales of convolution to extract the waveform characteristics of different scales,the max-pooling layer to prevent the disturbance of tiny noise causing false positives,combined with the feature of GRU helped to time-sequential modeling,made full use of the characteristics of data classification task.Compared with the state-of-the-art neural network model,the InnoHAR model has a promotion of 3% in the recognition accuracy,which has reached the state-of-the-art on the dataset we used,at the same time it still can guarantee the real-time prediction of low-power embedded platform,also with more space for future exploration.

Key words: human activity recognition, activity recognition, inception neural network, wearable sensor network, deep learning

CLC Number: 

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