Logo image
Back
An improved step counting algorithm using classification and double autocorrelation
Journal article   Peer reviewed

An improved step counting algorithm using classification and double autocorrelation

Sikha Bagui, Xingang Fang, Subhash Bagui, Jeremy Wyatt, Patrick Houghton, Joe Nguyen, John Schneider and Tyler Guthrie
International journal of computers & applications, Vol.44(3), pp.250-259
03/04/2022
DOI: https://doi.org/10.1080/1206212X.2020.1726006

Metrics

91 Record Views

Abstract

The objective of this paper was to develop an end-to-end algorithm that would improve the step counting accuracy in regular walking/running data and also meet the ANSI/CTA-2056 standards. The ANSI/CTA-2056 standards are to achieve an error rate of less than 10% on treadmill data on at least 20 participants. Our UWF-algorithm (UWFv1) has an improved step counting accuracy and also performs well below the acceptable ANSI/CTA-2056 error rate, using both treadmill data and non-treadmill data, hence our UWFv1 algorithm also meets the ANSI-CTA-2056 standards. For the end-to-end algorithm, the random forest model, trained on a feature engineered dataset, was chosen as the walking/running detection classifier, and double autocorrelation was recommended in the process of determining the step counts.

Details

Logo image