Smartphone-Based Human Activity Recognition Using CNN in Frequency Domain

Xiangyu Jiang, Yonggang Lu*, Zhenyu Lu, Huiyu Zhou

*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

Human activity recognition (HAR) based on smartphone sensors provides an efficient way for studying the connection between human physical activities and health issues. In this paper, three feature sets are involved, including tri-axial angular velocity data collected from gyroscope sensor, tri-axial total acceleration data collected from accelerometer sensor, and the estimated tri-axial body acceleration data. The FFT components of the three feature sets are used to divide activities into six types like walking, walking upstairs, walking downstairs, sitting, standing and lying. Two kinds of CNN architectures are designed for HAR. The one is Architecture A in which only one set of features is combined at the first convolution layer; and the other one is Architecture B in which two sets of the features are combined at the first convolution layer. The validation data set is used to automatically determine the iteration number during the training process. It is shown that the performance of Architecture B is better compared to Architecture A. And the Architecture B is further improved by varying the number of the features maps at each convolution layer and the one producing the best result is selected. Compared with five other HAR methods using CNN, the proposed method could achieve a better recognition accuracy of 97.5% for a UCI HAR dataset.

Original languageEnglish
Title of host publicationWeb and Big Data - APWeb-WAIM 2018 International Workshops
Subtitle of host publicationMWDA, BAH, KGMA, DMMOOC, 2018, Revised Selected Papers
EditorsLeong Hou U, Haoran Xie
PublisherSpringer Verlag
Pages101-110
Number of pages10
ISBN (Print)9783030012977
DOIs
Publication statusPublished - 2018
EventAsia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, APWeb-WAIM 2018 - Macau, China
Duration: 23 Jul 201825 Jul 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11268 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceAsia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint Conference on Web and Big Data, APWeb-WAIM 2018
CountryChina
CityMacau
Period23/07/201825/07/2018

Bibliographical note

Funding Information:
Acknowledgements. This work is supported by the National Key R&D Program of China (Grants No. 2017YFE0111900).

Publisher Copyright:
© 2018, Springer Nature Switzerland AG.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

Keywords

  • Accelerometer
  • Convolutional neural network
  • Gyroscope
  • Human activity recognition
  • Smartphones

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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