Dr Shadi Eltanani

Human gait is a significant indicator of overall health and well-being due to its dependence on metabolic requirements. Abnormalities in gait can indicate the presence of metabolic dysfunction, such as diabetes or obesity. However, detecting these can be challenging using classical methods, which often involve subjective clinical assessments or invasive procedures. In this work, a novel methodology known as Criticality Analysis (CA) was applied to the monitoring of the gait of teenagers with varying amounts of metabolic stress who are taking part in an clinical intervention to increase their activity and reduce overall weight. The CA approach analysed gait using inertial measurement units (IMU) by mapping the dynamic gait pattern into a nonlinear representation space. The resulting dynamic paths were then classified using a Support Vector Machine (SVM) algorithm, which is well-suited for this task due to its ability to handle nonlinear and dynamic data. The combination of the CA approach and the SVM algorithm demonstrated high accuracy and non-invasive detection of metabolic stress. It resulted in an average accuracy within the range of 78.2% to 90%. Additionally, at the group level, it was observed to improve fitness and health during the period of the intervention. Therefore, this methodology showed a great potential to be a valuable tool for healthcare professionals in detecting and monitoring metabolic stress, as well as other associated disorders.

The application of machine learning (ML) has made an unprecedented change in the field of medicine, showing a significant potential to automate tasks and to achieve objectives that are closer to human cognitive capabilities. Human gait, in particular, is a series of continuous metabolic interactions specific for humans. The need for an intelligent recognition of dynamic changes of gait enables physicians in clinical practice to early identify impaired gait and to reach proper decision making. Because of the underlying complexity of the biological system, it can be difficult to create an accurate detection and analysis of imbalanced gait. This paper proposes a novel Criticality Analysis (CA) methodology as a feasible method to extract the dynamic interactions involved in human gait. This allows a useful scale-free representation of multivariate dynamic data in a nonlinear representation space. To quantify the effectiveness of the CA methodology, a Support Vector Machine (SVM) algorithm is implemented in order to identify the nonlinear relationships and high-order interactions between multiple gait data variables. The gait features extracted from the CA method were used for training and testing the SVM algorithm. The simulation results of this paper show that the implemented SVM model with the support of the CA method increases the accuracy and enhances the efficiency of gait analysis to extremely high levels. Therefore, it can perform as a robust classification tool for detection of dynamic disturbances of biological data patterns and creates a tremendous opportunity for clinical diagnosis and rehabilitation.