Optimized Deep Learning-Based Pathological Gait Recognition Explored Through Network Analysis of Inertial Data

Human gait is a repetitive motor action that involves the coordination of various motor patterns controlled by a network of kinematic relations. Gait disorders, which can arise from neurological, muscular, or skeletal impairments, affect this coordination and lead to abnormal walking patterns. Pathological gait recognition (PGR) employs artificial intelligence methods to identify abnormal gaits by discriminating healthy from pathological motor patterns, thus helping clinicians to identify and monitor abnormal locomotor patterns. However, redundant input data, which may derive from similar motor patterns, can negatively impact on the model performance. Feature selection techniques are typically adopted to optimize the input dataset of AI-based frameworks and may rely on correlation analysis to identify redundant inputs. Graph theory can be used to provide a better understanding of the intricate relations and dependencies among input data. A paucity has been found in the literature about the exploitation of complex network analysis (CNA) as a method for optimizing the input dataset of a DL model oriented to PGR. This study employs CNA to optimize the performance of a PGR-oriented framework based on a DL architecture fed by inertial sensors. Despite the limited sample size, the model outcomes seems to confirm the effectiveness of complex network analysis in improving the performance in terms of prediction time by means of input data optimization.