Statistical analysis of window sizes and sampling rates in human activity recognition

Anzah H. Niazi, Delaram Yazdansepas, Jennifer L. Gay, Frederick W. Maier, Lakshmish Ramaswamy, Khaled Rasheed, Matthew Buman

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

6 Scopus citations

Abstract

Accelerometers are the most common device for data collection in the field of Human Activity Recognition (HAR). This data is recorded at a particular sampling rate and then usually separated into time windows before classification takes place. Though the sampling rate and window size can have a significant impact on the accuracy of the trained classifier, there has been relatively little research on their role in activity recognition. This paper presents a statistical analysis on the effect the sampling rate and window sizes on HAR data classification. The raw data used in the analysis was collected from a hip-worn Actigraphy G3X+ at 100Hz from 77 subjects performing 23 different activities. It was then re-sampled and divided into windows of varying sizes and trained using a single data classifier. A weighted least squares linear regression model was developed and two-way factorial ANOVA was used to analyze the effects of sampling rate and window size for different activity types and demographic categories. Based upon this analysis, we find that 10-second windows recorded at 50Hz perform statistically better than other combinations of window size and sampling rate.

Original languageEnglish (US)
Title of host publicationHEALTHINF 2017 - 10th International Conference on Health Informatics, Proceedings; Part of 10th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2017
EditorsHugo Gamboa, Ana Fred, Egon L. van den Broek, Mario Vaz
PublisherSciTePress
Pages319-325
Number of pages7
Volume5
ISBN (Electronic)9789897582134
StatePublished - Jan 1 2017
Event10th International Conference on Health Informatics, HEALTHINF 2017 - Part of 10th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2017 - Porto, Portugal
Duration: Feb 21 2017Feb 23 2017

Other

Other10th International Conference on Health Informatics, HEALTHINF 2017 - Part of 10th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2017
CountryPortugal
CityPorto
Period2/21/172/23/17

Keywords

  • Actigraph g3x+
  • Analysis of Variance
  • Body-worn Accelerometers
  • Data Mining
  • Human Activity Recognition
  • Random Forests
  • Sampling Rate
  • Weighted Least Squares
  • WEKA
  • Window Size

ASJC Scopus subject areas

  • Biomedical Engineering
  • Electrical and Electronic Engineering
  • Health Informatics
  • Health Information Management

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  • Cite this

    Niazi, A. H., Yazdansepas, D., Gay, J. L., Maier, F. W., Ramaswamy, L., Rasheed, K., & Buman, M. (2017). Statistical analysis of window sizes and sampling rates in human activity recognition. In H. Gamboa, A. Fred, E. L. van den Broek, & M. Vaz (Eds.), HEALTHINF 2017 - 10th International Conference on Health Informatics, Proceedings; Part of 10th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2017 (Vol. 5, pp. 319-325). SciTePress.