An improved step counting algorithm using classification and double autocorrelation. Issue 3 (4th March 2022)

• Record Type: Journal Article
• Authors: Bagui, Sikha; Fang, Xingang; Bagui, Subhash; Wyatt, Jeremy; Houghton, Patrick; Nguyen, Joe; Schneider, John; Guthrie, Tyler
• Is Part Of: International journal of computers and applications. Volume 44:Issue 3(2022)
• Page Start: 250
• Page End: 259
• Publication Date: 2022-03-04
• Subjects: Step counting -- regular walking/running data -- autocorrelation -- time-series data -- machine learning algorithms -- ANSI/CTA standards; Computers -- Periodicals; Computer software -- Periodicals; Computer networks -- Periodicals; Multimedia systems -- Periodicals; Internet -- Periodicals; World Wide Web -- Periodicals; Minicomputers -- Periodicals; Microcomputers -- Periodicals; 004.05
• DOI: 10.1080/1206212X.2020.1726006
• Languages: English
• ISSNs: 1206-212X

Abstract

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.