title: Estimation of temporal gait parameters using Bayesian models on acceleration signals.
abstract: The purpose of this study is to develop a system capable of performing calculation of temporal gait parameters using two low-cost wireless accelerometers and artificial intelligence-based techniques as part of a larger research project for conducting human gait analysis. Ten healthy subjects of different ages participated in this study and performed controlled walking tests. Two wireless accelerometers were placed on their ankles. Raw acceleration signals were processed in order to obtain gait patterns from characteristic peaks related to steps. A Bayesian model was implemented to classify the characteristic peaks into steps or nonsteps. The acceleration signals were segmented based on gait events, such as heel strike and toe-off, of actual steps. Temporal gait parameters, such as cadence, ambulation time, step time, gait cycle time, stance and swing phase time, simple and double support time, were estimated from segmented acceleration signals. Gait data-sets were divided into two groups of ages to test Bayesian models in order to classify the characteristic peaks. The mean error obtained from calculating the temporal gait parameters was 4.6%. Bayesian models are useful techniques that can be applied to classification of gait data of subjects at different ages with promising results.
authors: a Computer Science Department , Instituto Nacional de Astrofísica, Óptica y Electrónica , Puebla , Mexico.
authors: I H
authors: IH
authors: López-Nava
authors: a Computer Science Department , Instituto Nacional de Astrofísica, Óptica y Electrónica , Puebla , Mexico.
authors: A
authors: A
authors: Muñoz-Meléndez
authors: b Motion Analysis Laboratory, National Institute of Rehabilitation , Mexico City , Mexico.
authors: A I
authors: AI
authors: Pérez Sanpablo
authors: b Motion Analysis Laboratory, National Institute of Rehabilitation , Mexico City , Mexico.
authors: A
authors: A
authors: Alessi Montero
authors: b Motion Analysis Laboratory, National Institute of Rehabilitation , Mexico City , Mexico.
authors: I
authors: I
authors: Quiñones Urióstegui
authors: b Motion Analysis Laboratory, National Institute of Rehabilitation , Mexico City , Mexico.
authors: L
authors: L
authors: Núñez Carrera
mesh_terms: D000054:Acceleration; D061725:Accelerometry; D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D005260:Female; D005684:Gait / Q000502:physiology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D011336:Probability; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D055815:Young Adult
keywords: Bayesian models; acceleration signals; accelerometer sensor; gait analysis; gait parameters