An automated hierarchical gait pattern identification tool employing cross-correlation-based feature extraction and recurrent neural network based classification

In this paper Elman's recurrent neural network (ERNN) is employed for automatic identification of healthy and pathological gait and subsequent diagnosis of the neurological disorder in pathological gaits from the respective gait patterns. Stance, swing and double support intervals (expressed as percentages of stride) of 63 subjects were analysed for a period of approximately 300 s. The relevant gait features are extracted from cross-correlograms of these signals with corresponding signals of a reference subject. These gait features are used to train modular ERNNs performing binary and tertiary classifications. The average accuracy of binary classifiers is obtained as 90.6%-97.8% and that of tertiary classifiers is 89.8%. Hence, two hierarchical schemes are developed each of which uses more than one modular ERNN to segregate healthy, Parkinson's disease, Huntington's disease and amyotrophic lateral sclerosis subjects. The average testing performances of the schemes are 83.8% and 87.1%.