Gait analysis and machine learning classification on healthy subjects in normal walking. Issue 2 (4th March 2017)

• Record Type: Journal Article
• Authors: Shirakawa, Tomohiro; Sugiyama, Naruhisa; Sato, Hiroshi; Sakurai, Kazuki; Sato, Eri
• Is Part Of: International journal of parallel, emergent and distributed systems. Volume 32:Issue 2(2017)
• Page Start: 185
• Page End: 194
• Publication Date: 2017-03-04
• Subjects: gait analysis -- accelerometry -- machine learning -- healthy subject -- normal walking; Parallel computers -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Computer algorithms -- Periodicals; 004.35
• DOI: 10.1080/17445760.2015.1044007
• Languages: English
• ISSNs: 1744-5760

Abstract

Abstract : Walking is one of the most fundamental activities of human, and there have already been many studies on human walking. However, most of the studies so far mainly focus on the impaired gait of the patients with some disease or injury, and thus there are not many studies on the gait patterns of healthy subjects. In this study, we performed a gait analysis on 113 healthy subjects in normal walking and tried to classify their walking patterns using cluster analysis and principal component analysis. As a result, we got the basic data on the body movement of healthy walkers and a criterion for the classification and evaluation of unimpaired gait patterns.