Adaptive walk detection algorithm using activity counts

Matin Kheirkhahan, Zhiguo Chen, Duane B. Corbett, Amal Wanigatunga, Todd M. Manini, Sanjay Ranka

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

Abstract

Accelerometers have been the dominant device used for physical activity assessment studies. They are comfortable to wear at different locations and can accurately measure acceleration. Although, accurate methods for detecting walking in the lab and free-living condition using raw acceleration data exist, these algorithms are not useful for determining indoor movements that correspond to short walking bouts (< 2 minutes). In this paper, we present a new method that is adaptive to a small window of activity count data (10-15 seconds) and robust to within and between subject variability. The adaptive walking detection algorithm is evaluated using 22 adults and walks with a variety of durations ranging from 10 seconds to 8 minutes. The proposed algorithm showed high accuracy for all the walking periods and was significantly better for intervals shorter than 2 minutes.

Original languageEnglish (US)
Title of host publication2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages161-164
Number of pages4
ISBN (Electronic)9781509041794
DOIs
StatePublished - Apr 11 2017
Externally publishedYes
Event4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017 - Orlando, United States
Duration: Feb 16 2017Feb 19 2017

Publication series

Name2017 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017

Conference

Conference4th IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2017
CountryUnited States
CityOrlando
Period2/16/172/19/17

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering

Fingerprint Dive into the research topics of 'Adaptive walk detection algorithm using activity counts'. Together they form a unique fingerprint.

Cite this