title: Reliability and validity of gait analysis by android-based smartphone.
abstract: Smartphones are very common devices in daily life that have a built-in tri-axial accelerometer. Similar to previously developed accelerometers, smartphones can be used to assess gait patterns. However, few gait analyses have been performed using smartphones, and their reliability and validity have not been evaluated yet. The purpose of this study was to evaluate the reliability and validity of a smartphone accelerometer. Thirty healthy young adults participated in this study. They walked 20 m at their preferred speeds, and their trunk accelerations were measured using a smartphone and a tri-axial accelerometer that was secured over the L3 spinous process. We developed a gait analysis application and installed it in the smartphone to measure the acceleration. After signal processing, we calculated the gait parameters of each measurement terminal: peak frequency (PF), root mean square (RMS), autocorrelation peak (AC), and coefficient of variance (CV) of the acceleration peak intervals. Remarkable consistency was observed in the test-retest reliability of all the gait parameter results obtained by the smartphone (p<0.001). All the gait parameter results obtained by the smartphone showed statistically significant and considerable correlations with the same parameter results obtained by the tri-axial accelerometer (PF r=0.99, RMS r=0.89, AC r=0.85, CV r=0.82; p<0.01). Our study indicates that the smartphone with gait analysis application used in this study has the capacity to quantify gait parameters with a degree of accuracy that is comparable to that of the tri-axial accelerometer.
authors.affiliation: Department of Physical Therapy, Human Health Sciences, Kyoto University Graduate School of Medicine, Kyoto, Japan. shu.n@ks2.ecs.kyoto-u.ac.jp
authors.forename: Shu
authors.initials: S
authors.lastname: Nishiguchi
authors.forename: Minoru
authors.initials: M
authors.lastname: Yamada
authors.forename: Koutatsu
authors.initials: K
authors.lastname: Nagai
authors.forename: Shuhei
authors.initials: S
authors.lastname: Mori
authors.forename: Yuu
authors.initials: Y
authors.lastname: Kajiwara
authors.forename: Takuya
authors.initials: T
authors.lastname: Sonoda
authors.forename: Kazuya
authors.initials: K
authors.lastname: Yoshimura
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Yoshitomi
authors.forename: Hiromu
authors.initials: H
authors.lastname: Ito
authors.forename: Kazuya
authors.initials: K
authors.lastname: Okamoto
authors.forename: Tatsuaki
authors.initials: T
authors.lastname: Ito
authors.forename: Shinyo
authors.initials: S
authors.lastname: Muto
authors.forename: Tatsuya
authors.initials: T
authors.lastname: Ishihara
authors.forename: Tomoki
authors.initials: T
authors.lastname: Aoyama
mesh_terms: D000054:Acceleration; D000293:Adolescent; D000328:Adult; D001696:Biomechanical Phenomena; D005260:Female; D005684:Gait; D006801:Humans; D007564:Japan; D008297:Male; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D017216:Telemedicine / Q000295:instrumentation* / Q000379:methods; D055815:Young Adult