Walking (gait) irregularities and abnormalities are predictors and symptoms of disorder and disability.In the past, elaborate video (camera-based) systems, pressure mats, or a mix of the two has been used in clinical settings to monitor and evaluate gait.This article presents an artificial intelligence-based NEFF N1AHA02N0B N50 Built In 59cm Warming Drawer Stainless Steel comprehensive investigation of ground reaction force (GRF) pattern to classify the healthy control and gait disorders using the large-scale ground reaction force.The used dataset comprised GRF measurements from different patients.
The article includes machine learning- and deep learning-based Power Wire models to classify healthy and gait disorder patients using ground reaction force.A deep learning-based architecture GaitRec-Net is proposed for this classification.The classification results were evaluated using various metrics, and each experiment was analysed using a fivefold cross-validation approach.Compared to machine learning classifiers, the proposed deep learning model is found better for feature extraction resulting in high accuracy of classification.
As a result, the proposed framework presents a promising step in the direction of automatic categorization of abnormal gait pattern.