Self-gated recurrent neural networks for human activity recognition on wearable devices

Toan H. Vu, An Dang, Le Dung, Jia Ching Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

This paper develops a self-gated recurrent neural network (SGRNN), and applies it to human activity recognition (HAR), using timeseries signals collected from embedded sensors of wearable devices. Recurrent neural networks (RNNs) are very powerful for timeseries signal analysis. Especially, by integrating gates into recurrent units, gated RNNs such as LSTM and GRU are more complexity, and do not suffer from the vanishing gradient problem, so can learn very long-term dependencies. However, for use on wearable devices, RNNs must be simplified to reduce resource consumption, including memory usage and computational cost. The proposed model is approximately the same size and burdensome computation as that of a standard RNN, but exhibits explicit properties of the gating mechanism, so it is unaffected by the problem of vanishing gradients. Experimental results on the HAR problem not only demonstrate that the accuracy of our model is superior to that of the standard RNN, and is comparable with that of LSTM and GRU, but the model is low in resource consumption.

Original languageEnglish
Title of host publicationThematic Workshops 2017 - Proceedings of the Thematic Workshops of ACM Multimedia 2017, co-located with MM 2017
PublisherAssociation for Computing Machinery, Inc
Pages179-185
Number of pages7
ISBN (Electronic)9781450354165
DOIs
StatePublished - 23 Oct 2017
Event1st International ACM Thematic Workshops, Thematic Workshops 2017 - Mountain View, United States
Duration: 23 Oct 201727 Oct 2017

Publication series

NameThematic Workshops 2017 - Proceedings of the Thematic Workshops of ACM Multimedia 2017, co-located with MM 2017

Conference

Conference1st International ACM Thematic Workshops, Thematic Workshops 2017
Country/TerritoryUnited States
CityMountain View
Period23/10/1727/10/17

Keywords

  • Human activity recognition
  • Recurrent neural network
  • Self-gated recurrent neural network
  • Wearable devices

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