Temporal alignment improves feature quality: An experiment on activity recognition with accelerometer data

Hongjun Choi, Qiao Wang, Meynard Toledo, Pavan Turaga, Matthew Buman, Anuj Srivastava

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

2 Scopus citations

Abstract

Activity recognition has been receiving significant attention from a variety of research areas such as human performance enhancement, health promotion, and human computer interaction. However, recognizing activities from accelerometer data still remains a challenging problem due to sensitivity to sampling rates, misalignment of data, and increased variability in activities among clinically relevant populations. In order to solve these issues, we adopt methods from functional analysis, which consider non-elastic rate variations in movement. The overall framework factors out temporal variability within activity classes, before leveraging robust machine learning pipelines for a given end-use. The proposed approach has been evaluated on 7 classes of everyday activities with 50 subjects. The results indicate that proposed approach achieves improved performance with the improvements observed in separating similar classes that differ in temporal rates, and also demonstrate higher robustness to change in window lengths. These results suggest that temporal alignment should be considered a core part of activity recognition pipelines.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
PublisherIEEE Computer Society
Pages462-470
Number of pages9
Volume2018-June
ISBN (Electronic)9781538661000
DOIs
StatePublished - Dec 13 2018
Event31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 - Salt Lake City, United States
Duration: Jun 18 2018Jun 22 2018

Other

Other31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
CountryUnited States
CitySalt Lake City
Period6/18/186/22/18

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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    Choi, H., Wang, Q., Toledo, M., Turaga, P., Buman, M., & Srivastava, A. (2018). Temporal alignment improves feature quality: An experiment on activity recognition with accelerometer data. In Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018 (Vol. 2018-June, pp. 462-470). [8575537] IEEE Computer Society. https://doi.org/10.1109/CVPRW.2018.00075