An intelligent model for reconstruction of stance time from faulty gait recording

In an erroneous footfall ground-reaction force-time recording, which may occur for people with disabilities or frail elderly individuals, the stance time (ST) can be either corrupted or missing. Previous methods to estimate missing ST require force-time data from multiple force platforms and are affected by inter-step variability. This paper presents a model based on support vector machine (SVM) that is capable of estimating the missing ST from the available vertical force-timing characteristics with significantly high accuracy. The model was built using features taken from a data set of 466 sample trials of 27 subjects. A test on 40 sample trials drawn from all the subjects revealed an average prediction accuracy of 96.63% (±2.89%). In one-fourth of the test trials, the prediction error was within 1.0%. The model achieves considerable improvement over an artificial neural network based model built and tested on the same data set. The effect of kernel junction parameters and ε-insensitive loss function on prediction error is also analysed and presented.