Article

Gait patterns are often described by recording the changes in angular rotation of such joints as the hip, knee and ankle, during a complete cycle. Each joint exhibits distinctive behavior throughout the gait cycle, and abnormal gait can be described by measuring departure from a typical (mean) joint rotation curve. Standard techniques for observation of gait patterns produce large sets of data. Data reduction is achieved in this work by locating primary directions of variation from mean behavior. Variation from the mean can then be summarized with a one-dimensional statistic, thought of as a squared distance from the population mean. Percentiles of this one-dimensional index can be calculated, enabling classification of a child as normal, unusual or abnormal. A key feature of this analysis is that it is applied across multiple joint angle curves and their derivatives, thus providing a measure that takes account of the interactions between the curves as well as their individual characteristics. A data base of 348 gait cycles, collected from normal children, aged 3-7, were analyzed. Data on each child were stored in a 36-dimensional vector. Most information on patterns of variation among normal children can be stored in a smaller 11-dimensional vector, which can be used for diagnostic purposes. Performance of the one-dimensional index of gait is demonstrated on data from very young children, and on children,
up to age 7, who were born prematurely.

Langue : ANGLAIS

Identifiant basis : 2002224138