title: Algorithms to determine event timing during normal walking using kinematic data.
abstract: Algorithms to predict heelstrike and toeoff times during normal walking using only kinematic data are presented. The accuracy of these methods was compared with the results obtained using synchronized force platform recordings of two subjects walking at a variety of speeds for a total of 12 trials. Using a 60Hz data collection system, the absolute value errors (AVE) in predicting heelstrike averaged 4.7ms, while the AVE in predicting toeoff times averaged 5.6ms. True average errors (negative for an early prediction) were +1.2ms for both heelstrike and toeoff, indicating that no systematic errors occurred. It was concluded that the proposed algorithms provide an easy and reliable method of determining event times during walking when kinematic data are collected, with a considerable improvement in resolution over visual inspection of video records, and could be utilized in conjunction with any 2-D or 3-D kinematic data collection system.
authors.affiliation: Department of Kinesiology and Health Science, California State University, Sacramento, 6000 J Street, Sacramento, CA 95819-6073, USA. ahreljac@hhs4.hhs.csus.edu
authors.forename: A
authors.initials: A
authors.lastname: Hreljac
authors.forename: R N
authors.initials: RN
authors.lastname: Marshall
mesh_terms: D000054:Acceleration; D000465:Algorithms; D005260:Female; D005528:Foot / Q000502:physiology*; D005544:Forecasting; D006365:Heel / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D015203:Reproducibility of Results; D013997:Time Factors; D014034:Toes / Q000502:physiology; D016138:Walking / Q000502:physiology*; D016474:Weight-Bearing / Q000502:physiology