A vision‐based clinical analysis for classification of knee osteoarthritis, Parkinson's disease and normal gait with severity based on k‐nearest neighbour

The prevalence of musculoskeletal and neurological diseases such as knee osteoarthritis (KOA) and Parkinson's disease (PD) has grown speedily in recent years. The direct impact of these disorders on a person's gait has evoked many researchers to perform their analysis using gait because of its greater feasibility. In this paper, we proposed a framework to accurately classify abnormal considering KOA and PD and normal (NM) gait using a vision-based (VB) approach. The aim of this study is four-fold: firstly, a novel VB gait dataset is created and presented; secondly, the segmentation of the region of interest (ROIs) is performed using an improved technique; thirdly gait parameters, that is, spatiotemporal (SPT), linear kinematic (KNM) and additional body motion features are evaluated using statistical methods; fourthly, the classification of KOA, PD and NM subjects has been done based on artificial intelligence or a machine learning technique (MLT), that is, k-nearest neighbour (KNN) at three severity levels and a comparison is performed with other methods namely support vector machine (SVM), random forest (RF) and linear regression (LR). The results reveal the relevance of considered gait features in this investigation. For classification purposes, the highest results are obtained by our proposed method in classifying the subjects with severity also, achieving an overall accuracy = 0.9006, sensitivity = 0.8554, specificity = 0.9044, and precision = 0.8966 respectively. This study thus successfully presents KOA, PD, and NM gait classification method based on the MLT which may be beneficial for clinicians to perform early diagnosis of such disorders objectively.