The use of neural networks to recognize patterns of human movement: gait patterns

Artificial neural networks and a statistical method, linear discriminant analysis, were both applied to the recognition of temporal gait parameters associated with altered gait patterns. The duration of the double support and right and left single support phases were measured at seven speeds and three walking conditions. Data from 10 subjects were used to train neural networks, which were then tested using data from 10 other subjects. The overall performance of the networks was at least as high as that of linear discriminant analysis. The relative ease with which neural networks can be set up in a computer, and their discriminatory power, suggests that the technique has a useful role to play in gait analysis. RELEVANCE: The capacity of neural networks to recognize alteration of gait patterns suggests that they might provide an alternative approach for gait assessment. They might be proved to be a useful diagnostic tool.