Using Amplitude Modulation for Extracting Gait Features

Feature extraction for gait analysis has been explored widely over the past years. The set of static and/or dynamic skeleton parameters which are obtained from tracking body joints (i.e. limbs and trunk) are initially pool of gait features extraction. The challenge of gait feature extraction is to reduce the noise in the row data which is due the computational complexity of determination of the gait cycle and sub-phases of the gait cycle, correctly. Although marker-based motion capture systems are highly accurate, they often only used in laboratory environments which leads to a constrained method. Alternative products such as MS Kinect overcome the limitations of the motion capture systems by providing low-cost, moderate accuracy with flexibility of quick installation even in residential settlements. The level of accuracy of the MS Kinect camera for 3D skeleton points can be increased by using pre-processing techniques which helps to overcome the jitter and nose in data. The proposed method modifies the gait walk signal using amplitude modulation (AM) technique to extract high predictive power of gait features without the need of gait cycle determination. Experimental results on 14 health subjects and 3 different types of walking speeds shows that AM technique provides 100% correctly classified instances using support vector machine (SVM) and decision tree (DT) classifiers, while 97.6% with k-nearest neighbour (k-NN) classifier.