Agility demands of Gaelic football match-play: a time-motion analysis

Research into the physical demands of Gaelic football is limited with no research into the agility or change of direction (CoD) demands of the sport. This study examined the CoD demands of Gaelic football via a time-motion analysis of senior inter-county match play. The Bloomfield movement classification (BMC) was adapted for application to Gaelic football. A new “descriptor” was used in an effort to account for the decision-making component of agility by isolating actions that occurred during active engagement with play. Of 1,899 changes of direction (CoDs) identified, 1,035 occurred during active engagement in play. The left/right split for CoDs during active engagement in play was 47.1/49.9%, indicating no preference for completing actions to one side over the other. Whilst the most common CoDs were ≤90° (74.9%), 80% of CoDs greater than 270° took place during active engagement in play. CoD actions are very common in Gaelic football and may be more common than in other field and court sports. It is important that athletes are physically prepared to cope with the demands of very acute CoDs during meaningful periods of match play

A Sensitivity Analysis of Poisoning and Evasion Attacks in Network Intrusion Detection System Machine Learning Models

As the demand for data has increased, we have witnessed a surge in the use of machine learning to help aid industry and government in making sense of massive amounts of data and, subsequently, making predictions and decisions. For the military, this surge has manifested itself in the Internet of Battlefield Things. The pervasive nature of data on today\u27s battlefield will allow machine learning models to increase soldier lethality and survivability. However, machine learning models are predicated upon the assumptions that the data upon which these machine learning models are being trained is truthful and the machine learning models are not compromised. These assumptions surrounding the quality of data and models cannot be the status-quo going forward as attackers establish novel methods to exploit machine learning models for their benefit. These novel attack methods can be described as adversarial machine learning (AML). These attacks allow an attacker to unsuspectingly alter a machine learning model before and after model training in order to degrade a model\u27s ability to detect malicious activity. In this paper, we show how AML, by poisoning data sets and evading well trained models, affect machine learning models\u27 ability to function as Network Intrusion Detection Systems (NIDS). Finally, we highlight why evasion attacks are especially effective in this setting and discuss some of the causes for this degradation of model effectiveness

Machine learning prediction of combat basic training injury from 3D body shape images.

IntroductionAthletes and military personnel are both at risk of disabling injuries due to extreme physical activity. A method to predict which individuals might be more susceptible to injury would be valuable, especially in the military where basic recruits may be discharged from service due to injury. We postulate that certain body characteristics may be used to predict risk of injury with physical activity.MethodsUS Army basic training recruits between the ages of 17 and 21 (N = 17,680, 28% female) were scanned for uniform fitting using the 3D body imaging scanner, Human Solutions of North America at Fort Jackson, SC. From the 3D body imaging scans, a database consisting of 161 anthropometric measurements per basic training recruit was used to predict the probability of discharge from the US Army due to injury. Predictions were made using logistic regression, random forest, and artificial neural network (ANN) models. Model comparison was done using the area under the curve (AUC) of a ROC curve.ResultsThe ANN model outperformed two other models, (ANN, AUC = 0.70, [0.68,0.72], logistic regression AUC = 0.67, [0.62,0.72], random forest AUC = 0.65, [0.61,0.70]).ConclusionsBody shape profiles generated from a three-dimensional body scanning imaging in military personnel predicted dischargeable physical injury. The ANN model can be programmed into the scanner to deliver instantaneous predictions of risk, which may provide an opportunity to intervene to prevent injury