Abnormal gait detection using lean and ramp angle features

Abstract

Gait Recognition plays a major role in Person Recognition in remote monitoring applications. In this paper, a new gait recognition approach has been proposed to recognize the normal/abnormal gait of the person with improved feature extraction techniques. In order to identify the abnormality of the person lean angle and ramp angle has been considered as features. The novelty of the paper lies in the feature extraction phase where the walking abnormality is measured based on foot movement and lean angle which is measured between the head and the hip. In this paper, the feature vector is composed of measured motion cues information such as lean angle between the head and the hip and ramp angle between the heel and the toe for two legs. These features provide the information about postural stability and heel strike stability respectively. In the training phase, classifier has been trained using normal gait sequences by exemplar calculation for each video and distance metric between the sequences separately for samples. Based on the distance metric, the maximum distance has been calculated and considered as threshold value of the classifier. In the testing phase, distance vector between training samples and testing samples has been calculated to classify normal or abnormal gait by using threshold based classifier. Performance of this system has been tested over different data and the results seem to be promising.