Abnormal locomotor patterns may occur in case of either motor damages or neurological conditions, thus potentially jeopardizing an individual’s safety. Pathological gait recognition (PGR) is a research field that aims to discriminate among different walking patterns. A PGR-oriented system may benefit from the simulation of gait disorders by healthy subjects, since the acquisition of actual pathological gaits would require either a higher experimental time or a larger sample size. Only a few works have exploited abnormal walking patterns, emulated by unimpaired individuals, to perform PGR with Deep Learning-based models. In this article, the authors present a workflow based on convolutional neural networks to recognize normal and pathological locomotor behaviors by means of inertial data related to nineteen healthy subjects. Although this is a preliminary feasibility study, its promising performance in terms of accuracy and computational time pave the way for a more realistic validation on actual pathological data. In light of this, classification outcomes could support clinicians in the early detection of gait disorders and the tracking of rehabilitation advances in real time.

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.