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.

Unmanned Aerial Vehicles (UAVs) are considered, nowadays, as a futuristic and robust paradigm for 5G wireless networks, in terms of providing Internet connectivity services onto infrastructure cellular networks. In this paper, the interference regime caused by multiple downlink aerial wireless transmission beams has been highlighted. This has been introduced by estimating the UAVs coverage area that is analytically derived in a tractable closed-form expression. The rationale of the analysed coverage approach relies on observing and adapting the joint aerial distance between the aerial base stations. This can minimize the intra-overlapped coverage and ultimately maximize the overall coverage performance for a better quality of service demands. The novelty of our approach brings useful design insights for UAVs system-level performance that technically helps in aerial coverage computations without the need of performing an aerial deployment setup. To the end, the performance effectiveness of our methodology has been tested under an urban propagation environment conditions, in which the original probabilistic channel model approximation has been taken into account. Moreover, this paper identifies the interference issue of such an aerial network as a shrinkage or distortion phenomenon.

Gait is a well-known motive means for humans. It is both energetically demanding and reflects several of human physical, mental and energetic disorders. Detecting these abnormalities can help medical professionals for better modelling and detection of biosystem chronic diseases, which enable timely treatment of patients and help control of the diseases' spread. In this paper, K-Nearest Neighbour (KNN) machine learning classification algorithm highlights the comparison between the gait patterns of normal healthy individuals and the patients suffering from irregular gait patterns caused by physical disorder conditions, including strapped muscles. Moreover, the Cross Validation test has addressed to examine how accurately the model fits the real-world clinical data. The experimental results show that the KNN algorithm can effectively be a robust learning classifier in classifying normal and abnormal human gait features. The classification performance of our proposed model is 67.7%, and its effectiveness has evaluated at a minimum square error rate.

Unmanned Aerial Vehicles (UAVs), commercially known as drones, have received great attention. This is due to their versatility and applicability to a large number of domains such as surveillance system, aerial photography, traffic control, flyable base stations to provide a broadband coverage and even for future urban transportation services. In this paper, the optimal distance between multiple aerial base stations has analytically been derived, based on an aerial coverage area computation. This is a fundamental wireless metric that can significantly minimise the intra-overlapped coverage and also can enhance wireless coverage connectivity and performance of aerial wireless networks. The novelty of our approach brings a better aerial optimal design understanding for UAVs communications performance without the need for establishing an aerial deployment setup.

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.