FEEDBACK-BASED GAIT IDENTIFICATION USING DEEP NEURAL NETWORK CLASSIFICATION
Unknown authors
Abstract
Identification of gait plays a major role in the healthcare industry, recognition of a gait having different angles, identification of abnormalities is a challenging task, to detect the abnormal person identification contains improper pattern style, human limbs, walking pattern, etc... A normal person has a correct pattern, an abnormal person has an irregular pattern. This paper provides the identification of the lean angle and ramp angle [19] of irregular patterns on three abnormalities such as Parkinson gait, Hemiplegic gait, and Neuropathic gait [18] by using deep neural network (DNN) without clinical observation by using DNN classification with feedback-based verification of trained features with query features of abnormal identification of trained features with query features. This paper concludes the gait abnormalities based on lean angle and ramp angle.