Description:
The purpose of this paper is to summarise the methodology for the 5th International Consensus Conference on Concussion in Sport. The 18 months of preparation included engagement of a scientific committee, an expert panel of 33 individuals in the field of concussion and a modified Delphi technique to determine the primary questions to be answered. The methodology also involved the writing of 12 systematic reviews to inform the consensus conference and submission and review of scientific abstracts. The meeting itself followed a 2-day open format, a 1-day closed expert panel meeting and two additional half day meetings to develop the Concussion Recognition Tool 5 (Pocket CRT5), Sport Concussion Assessment Tool 5 (SCAT5) and Child SCAT5.
Description:
Shuffle-exchange networks (SENs) have been widely considered as practical interconnection systems due to their size of its switching elements (SEs) and uncomplicated configuration. SEN is a network among a large class of topologically equivalent multistage interconnection networks (MINs) that includes omega, indirect binary n-cube, baseline, and generalized cube. In this paper, SEN with additional stages that provide more redundant paths are analyzed. A common network topology with a 2×2 basic building block in a SEN and its variants in terms of extra-stages is investigated. As an illustration, three types of SENs are compared: SEN, SEN with an additional stage (SEN+), and SEN with two additional stages (SEN+2). Finally, three measures of reliability: terminal, broadcast, and network reliability for the three SEN systems are analyzed.
Description:
Objective: To compare Orchard Sports Injury Classification System (OSICS-10) sports medicine diagnoses assigned by a clinical and non-clinical coder. Design: Assessment of intercoder agreement. Setting: Community Australian football. Participants: 1082 standardised injury surveillance records. Main outcome measurements: Direct comparison of the four-character hierarchical OSICS-10 codes assigned by two independent coders (a sports physician and an epidemiologist). Adjudication by a third coder (biomechanist). Results: The coders agreed on the first character 95% of the time and on the first two characters 86% of the time. They assigned the same four-digit OSICS-10 code for only 46% of the 1082 injuries. The majority of disagreements occurred for the third character; 85% were because one coder assigned a non-specific 'X' code. The sports physician code was deemed correct in 53% of cases and the epidemiologist in 44%. Reasons for disagreement included the physician not using all of the collected information and the epidemiologist lacking specific anatomical knowledge. Conclusions: Sports injury research requires accurate identification and classification of specific injuries and this study found an overall high level of agreement in coding according to OSICS-10. The fact that the majority of the disagreements occurred for the third OSICS character highlights the fact that increasing complexity and diagnostic specificity in injury coding can result in a loss of reliability and demands a high level of anatomical knowledge. Injury report form details need to reflect this level of complexity and data management teams need to include a broad range of expertise. Copyright Article author (or their employer) 2012.
Description:
Objective: To compare Orchard Sports Injury Classification System (OSICS-10) sports medicine diagnoses assigned by a clinical and non-clinical coder. Design: Assessment of intercoder agreement. Setting: Community Australian football. Participants: 1082 standardised injury surveillance records. Main outcome measurements: Direct comparison of the four-character hierarchical OSICS-10 codes assigned by two independent coders (a sports physician and an epidemiologist). Adjudication by a third coder (biomechanist). Results: The coders agreed on the first character 95% of the time and on the first two characters 86% of the time. They assigned the same four-digit OSICS-10 code for only 46% of the 1082 injuries. The majority of disagreements occurred for the third character; 85% were because one coder assigned a non-specific 'X' code. The sports physician code was deemed correct in 53% of cases and the epidemiologist in 44%. Reasons for disagreement included the physician not using all of the collected information and the epidemiologist lacking specific anatomical knowledge. Conclusions: Sports injury research requires accurate identification and classification of specific injuries and this study found an overall high level of agreement in coding according to OSICS-10. The fact that the majority of the disagreements occurred for the third OSICS character highlights the fact that increasing complexity and diagnostic specificity in injury coding can result in a loss of reliability and demands a high level of anatomical knowledge. Injury report form details need to reflect this level of complexity and data management teams need to include a broad range of expertise. Copyright Article author (or their employer) 2012.
Description:
Incipient fault detection in low signal-to-noise ratio (SNR) conditions requires robust features for accurate condition-based machine health monitoring. Accurate fault classification is positively linked to the quality of features of the faults. Therefore, there is a need to enhance the quality of the features before classification. This paper presents a novel vibration spectrum imaging (VSI) feature enhancement procedure for low SNR conditions. An artificial neural network (ANN) has been used as a fault classifier using these enhanced features of the faults. The normalized amplitudes of spectral contents of the quasi-stationary time vibration signals are transformed into spectral images. A 2-D averaging filter and binary image conversion, with appropriate threshold selection, are used to filter and enhance the images for the training and testing of the ANN classifier. The proposed novel VSI augments and provides the visual representation of the characteristic vibration spectral features in an image form. This provides enhanced spectral images for ANN training and thus leads to a highly robust fault classifier.
Description:
Industrial Internet of Things (IIoT) deploys edge devices to act as intermediaries between sensors and actuators and application servers or cloud services. Machine learning models have been widely used to thwart malware attacks in such edge devices. However, these models are vulnerable to adversarial attacks where attackers craft adversarial samples by introducing small perturbations to malware samples to fool a classifier to misclassify them as benign applications. Literature on deep learning networks proposes adversarial retraining as a defense mechanism where adversarial samples are combined with legitimate samples to retrain the classifier. However, existing works select such adversarial samples in a random fashion which degrades the classifier's performance. This work proposes two novel approaches for selecting adversarial samples to retrain a classifier. One, based on the distance from malware cluster center, and the other, based on a probability measure derived from a kernel based learning (KBL). Our experiments show that both of our sample selection methods outperform the random selection method and the KBL selection method improves detection accuracy by 6%. Also, while existing works focus on deep neural networks with respect to adversarial retraining, we additionally assess the impact of such adversarial samples on other classifiers and our proposed selective adversarial retraining approaches show similar performance improvement for these classifiers as well. The outcomes from the study can assist in designing robust security systems for IIoT applications.
Description:
Industrial Internet of Things (IIoT) deploys edge devices to act as intermediaries between sensors and actuators and application servers or cloud services. Machine learning models have been widely used to thwart malware attacks in such edge devices. However, these models are vulnerable to adversarial attacks where attackers craft adversarial samples by introducing small perturbations to malware samples to fool a classifier to misclassify them as benign applications. Literature on deep learning networks proposes adversarial retraining as a defense mechanism where adversarial samples are combined with legitimate samples to retrain the classifier. However, existing works select such adversarial samples in a random fashion which degrades the classifier's performance. This work proposes two novel approaches for selecting adversarial samples to retrain a classifier. One, based on the distance from malware cluster center, and the other, based on a probability measure derived from a kernel based learning (KBL). Our experiments show that both of our sample selection methods outperform the random selection method and the KBL selection method improves detection accuracy by 6%. Also, while existing works focus on deep neural networks with respect to adversarial retraining, we additionally assess the impact of such adversarial samples on other classifiers and our proposed selective adversarial retraining approaches show similar performance improvement for these classifiers as well. The outcomes from the study can assist in designing robust security systems for IIoT applications.
Description:
The authors acknowledge the Capability Development Fund Scheme of RMIT University, the Maxwell Eagle Endowment Award and the CASS Foundation Science/Medicine Grant for financial support. N.T. is a recipient of an RMIT Vice-Chancellor’s Research Fellowship.
Description:
Objective: To see whether concerns about injury risk relate to children's physical activity (PA). Methods: Two cohorts were recruited from 19 Australian schools and assessed in 2001 (T1), 2004 (T2) and 2006 (T3). The younger (n=162) was assessed at 6, 9 and 11years old, and the older (n=259) at 11, 14 and 16 years old. At T1 and T2, parents of the younger cohort reported on fear of child being injured, and whether child would be at risk of injury if they played organised sport; the older cohort self-reported injury fear. Accelerometers assessed PA at each time point. Linear regression models examined cross-sectional associations, and also associations between T1 injury fear and risk and T2 PA, and T2 injury fear and risk and T3 PA. Results: In the younger cohort at T2 (9 years), fear and risk were both negatively associated with moderate to vigorous PA (MVPA) (β=−0.17, 95% CI −0.30 to −0.03 and β=−0.26, 95% CI −0.41 to −0.10) and also vigorous PA (VPA). Fear was also associated with moderate PA (MPA). For the older cohort at T1, injury fear was negatively associated with MVPA (β=−0.21, 95% CI −0.35 to −0.07) and also MPA and VPA. Parental perception of risk at T1 (6 years) was negatively associated with children’s MPA at T2 (9 years) (β= −0.17, 95% CI −0.32 to −0.02). Sex did not moderate any association. Conclusions Younger children and their parents need to know which sports have low injury risks. Some children may need increased confidence to participate.
Description:
Objective: To see whether concerns about injury risk relate to children's physical activity (PA). Methods: Two cohorts were recruited from 19 Australian schools and assessed in 2001 (T1), 2004 (T2) and 2006 (T3). The younger (n=162) was assessed at 6, 9 and 11years old, and the older (n=259) at 11, 14 and 16 years old. At T1 and T2, parents of the younger cohort reported on fear of child being injured, and whether child would be at risk of injury if they played organised sport; the older cohort self-reported injury fear. Accelerometers assessed PA at each time point. Linear regression models examined cross-sectional associations, and also associations between T1 injury fear and risk and T2 PA, and T2 injury fear and risk and T3 PA. Results: In the younger cohort at T2 (9 years), fear and risk were both negatively associated with moderate to vigorous PA (MVPA) (
Effectiveness of online tailored advice to prevent running-related injuries and promote preventive behaviour in Dutch trail runners : A pragmatic randomised controlled trial
Description:
BACKGROUND: Trail running is popular worldwide, but there is no preventive intervention for running-related injury (RRI). AIM: To evaluate the effectiveness of adding online tailored advice (TrailS6 ) to general advice on (1) the prevention of RRIs and (2) the determinants and actual preventive behaviour in Dutch trail runners. METHODS: Two-arm randomised controlled trial over 6 months. 232 trail runners were randomly assigned to an intervention or control group. All participants received online general advice on RRI prevention 1 week after baseline. Every 2 weeks, participants in the intervention group received specific advice tailored to their RRI status. The control group received no further intervention. Bayesian mixed models were used to analyse the data. RESULTS: Trail runners in the intervention group sustained 13% fewer RRIs compared with those in the control group after 6 months of follow-up (absolute risk difference -13.1%, 95% Bayesian highest posterior credible interval (95% BCI) -23.3 to -3.1). A preventive benefit was observed in one out of eight trail runners who had received the online tailored advice for 6 months (number needed to treat 8, 95% BCI 3 to 22). No significant between-group difference was observed on the determinants and actual preventive behaviours. CONCLUSIONS: Online tailored advice prevented RRIs among Dutch trail runners. Therefore, online tailored advice may be used as a preventive component in multicomponent RRI prevention programmes. No effect was observed on determinants and actual preventive behaviours. TRIAL REGISTRATION NUMBER: The Netherlands National Trial Register (NTR5431).
Description:
BACKGROUND: Trail running is popular worldwide, but there is no preventive intervention for running-related injury (RRI). AIM: To evaluate the effectiveness of adding online tailored advice (TrailS6 ) to general advice on (1) the prevention of RRIs and (2) the determinants and actual preventive behaviour in Dutch trail runners. METHODS: Two-arm randomised controlled trial over 6 months. 232 trail runners were randomly assigned to an intervention or control group. All participants received online general advice on RRI prevention 1 week after baseline. Every 2 weeks, participants in the intervention group received specific advice tailored to their RRI status. The control group received no further intervention. Bayesian mixed models were used to analyse the data. RESULTS: Trail runners in the intervention group sustained 13% fewer RRIs compared with those in the control group after 6 months of follow-up (absolute risk difference -13.1%, 95% Bayesian highest posterior credible interval (95% BCI) -23.3 to -3.1). A preventive benefit was observed in one out of eight trail runners who had received the online tailored advice for 6 months (number needed to treat 8, 95% BCI 3 to 22). No significant between-group difference was observed on the determinants and actual preventive behaviours. CONCLUSIONS: Online tailored advice prevented RRIs among Dutch trail runners. Therefore, online tailored advice may be used as a preventive component in multicomponent RRI prevention programmes. No effect was observed on determinants and actual preventive behaviours. TRIAL REGISTRATION NUMBER: The Netherlands National Trial Register (NTR5431).