Assessing impacts of data volume and data set balance in using deep learning approach to human activity recognition

Haipeng Chen, Fuhai Xiong, Dihong Wu, Lingxiang Zheng, Ao Peng, Xuemin Hong, Biyu Tang, Hai Lu, Haibin Shi, Huiru Zheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)
12 Downloads (Pure)
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages1160-1165
Number of pages6
ISBN (Print)978-1-5090-3051-4
DOIs
Publication statusE-pub ahead of print - 18 Dec 2017
EventDLB2H 2017 workshop at 2017 IEEE International Conference on Bioinformatics and Biomedicine -
Duration: 18 Dec 2017 → …

Workshop

WorkshopDLB2H 2017 workshop at 2017 IEEE International Conference on Bioinformatics and Biomedicine
Period18/12/17 → …

Keywords

  • human activity recognition
  • deep learning
  • LSTM
  • CNN

Cite this

Chen, H., Xiong, F., Wu, D., Zheng, L., Peng, A., Hong, X., Tang, B., Lu, H., Shi, H., & Zheng, H. (2017). Assessing impacts of data volume and data set balance in using deep learning approach to human activity recognition. In Unknown Host Publication (pp. 1160-1165). IEEE. https://doi.org/10.1109/BIBM.2017.8217821