Combining feature ranking with PCA: An application to gait analysis

Feature reduction is an effective way to improve the classification performance when machine learning methods are used in gait analysis. In this paper, we proposed a novel hybrid feature reduction method (MSNR&PCA) based on the combination of feature ranking with principle component analysis (PCA). Three feature reduction methods, namely, feature ranking based the value of signal to noise ratio (MSNR), PCA and the proposed hybrid approach (MSNR&PCA), were examined in two gait analysis problems. One gait analysis problem is to differentiate the patients with Neurodegenerative disease from the controls based on the gait data collected by footswitches. The other problem is to discriminate the patients with complex regional pain syndrome (CRPS) from controls based on the gait data collected by an accelerometer. Results showed that the proposed MSNR&PCA achieved best classification performance in two gait datasets. In footswitch data, the highest accuracy (81.78%) was obtained using a feature subset with 4 features generated from original 10 features by MSNR&PCA. In the accelerometer dataset, classification with three features generated from 17 features by MSNR&PCA achieved the best performance with an accuracy of 100%.