Networks of inter-organisational coordination during disease outbreaks

Multi-organisational environment is demonstrating more complexities due the ever-increasing tasks’ complications in modern environments. Disease outbreak coordination is one of these complex tasks that require multi-skilled and multi-jurisdictional agencies to coordinate in dynamic environment. This research discusses theoretical foundations and practical approaches to suggest frameworks to study complex inter-organisational networks in dynamic environments, specifically during disease outbreak. We study coo¬¬rdination as being an interdisciplinary domain, and then uses social network theory to model it. I have surveyed 70 health professionals whom have participated in the swine influenza H1N1 2009 outbreak. I collected both qualitative and quantitative data in order to build a comprehensive understanding of the dynamics of the inter-organisational network that evolved during that outbreak. Then I constructed a performance model by use three main components of the network theory: degree centrality, connectedness and tie strength as the independent variables, and disease outbreak inter-organisational performance as the dependent one. In addition, we study both the formal networks and the informal ones. Formal networks are based on the standard operating structures, and the informal ones emerge based on trust, mutual benefits and relationships. Results suggest that the proposed social network measures have positive effect on coordination performance during the outbreak in both formal and informal networks, except centrality in the formal one. In addition, none of those measures influence performance before the outbreak. Practically, the results suggest that increasing the communication frequency and diversifying the tiers of the inter-organisational links enhance the overall network’s performance in formal coordination. In the informal one, links are created with the intention to improve performance; hence, all suggested network measures improve performance

Welke rol speelt sports analytics in het voetbal van morgen?

Sports Analytics is aan een stevige opmars bezig in het voetbal. Het einde van de reguliere Belgische voetbalcompetitie bood ons half maart de ideale gelegenheid om de Belgische voetbalsupporters warm te maken voor deze discipline. We berekenden voor iedere voetbalclub dertien parameters die een beeld geven van onder andere hun dominantie, aanvallende ingesteldheid, efficiëntie voor doel en agressiviteit. De markantste vaststellingen uit onze analyse werden duidelijk gesmaakt en kregen aandacht in de toonaangevende voetbalmedia.status: publishe

Prestatie-analyse van de clubs in de Belgische Pro League 2013-2014: de play-offs doorgelicht

Dit rapport bevat een prestatie-analyse van de play-offs van de Belgische voetbalcompetitie voor het seizoen 2013-2014. De gedetailleerde statistieken die voetbalwebsite Soccerway dit voetbalseizoen voor het eerst beschikbaar stelde voor de Belgische competitie werden gecombineerd tot dertien parameters die een inzicht verschaffen in de dominantie, aanvallende ingesteldheid, efficiëntie voor doel en agressiviteit van iedere club. Het rapport vergelijkt de prestaties van de clubs in play-off I en play-off II met die in de reguliere competitie.nrpages: 57status: publishe

Prestatie-analyse van de clubs in de Belgische Pro League 2013-2014: de reguliere competitie doorgelicht

Dit rapport bevat een prestatie-analyse van de reguliere Belgische voetbalcompetitie voor het seizoen 2013-2014. De gedetailleerde statistieken die voetbalwebsite Soccerway dit voetbalseizoen voor het eerst beschikbaar stelde voor de Belgische competitie werden gecombineerd tot dertien parameters die een inzicht verschaffen in de dominantie, aanvallende ingesteldheid, efficiëntie voor doel en agressiviteit van iedere club. De analyse heeft tot een aantal opvallende resultaten geleid. Standard sloot de reguliere competitie af als leider, maar bekleedt pas de tiende plaats op de lijst van meest dominante clubs. De club compenseert het mindere balbezit echter met een enorme doelgerichtheid en doet op dat vlak beter dan alle andere eersteklassers. OH Leuven voert met voorsprong het klassement van geïncasseerde rode kaarten aan, terwijl de spelers van Anderlecht opvallend weinig met een gele kaart bestraft worden.nrpages: 30status: publishe

Automating Feature Construction for Multi-View Time Series Data

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Monitoring the crus for physical therapy

Capturing the movements of a patient performing a rehabilitation exercise currently involves an extensive lab setup. The goal of this study is to investigate whether a 3D camera, such as the Microsoft Kinect (TM), can be used to monitor patients locally. Specifically we are interested in the lower limbs since most 3D camera algorithms focus on the upper body while for rehabilitation, the lower body is crucial. This paper presents two particle-filtering based algorithms for accurate tracking. The first algorithm estimates the configuration of the lower limbs simultaneously while the second one estimates the configuration of one limb at a time. We compare our estimates with a gold standard and find that we are able to recognize most movement characteristics. Furthermore, our approach is better at tracking the height of the foot and yields more stable tracking results than the NITE skeleton tracker.status: publishe

AMIE: Automatic Monitoring of Indoor Exercises

Patients with sports-related injuries need to learn to perform rehabilitative exercises with correct movement patterns. Unfortunately, the feedback a physiotherapist can provide is limited by the visitation frequency of the patient. We study the feasibility of a system that automatically provides feedback on correct movement patterns to patients using a Microsoft Kinect camera and Machine Learning techniques. We discuss several challenges related to the Kinect's proprietary software, the Kinect data's heterogeneity, and the Kinect data's temporal component. We introduce AMIE, a machine learning pipeline that detects the exercise being performed, the exercise's correctness, and if applicable, the mistake that was made. To evaluate AMIE, ten participants were instructed to perform three types of typical rehabilitation exercises (squats, forward lunges and side lunges) demonstrating both correct movement patterns and frequent types of mistakes, while being recorded with a Kinect. AMIE detects the type of exercise almost perfectly with 99% accuracy and the type of mistake with 73% accuracy.status: publishe

Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion

Running is extremely popular and around 10.6 million people run regularly in the United States alone. Unfortunately, estimates indicated that between 29% to 79% of runners sustain an overuse injury every year. One contributing factor to such injuries is excessive fatigue, which can result in alterations in how someone runs that increase the risk for an overuse injury. Thus being able to detect during a running session when excessive fatigue sets in, and hence when these alterations are prone to arise, could be of great practical importance. In this paper, we explore whether we can use machine learning to predict the rating of perceived exertion (RPE), a validated subjective measure of fatigue, from inertial sensor data of individuals running outdoors. We describe how both the subjective target label and the realistic outdoor running environment introduce several interesting data science challenges. We collected a longitudinal dataset of runners, and demonstrate that machine learning can be used to learn accurate models for predicting RPE.status: publishe

Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running

Maximal oxygen uptake (VO2max) is often used to assess an individual’s cardiorespiratory fitness. However, measuring this variable requires an athlete to perform a maximal exercise test which may be impractical, since this test requires trained staff and specialized equipment, and may be hard to incorporate regularly into training programs. The aim of this study is to develop a new model for predicting VO2max by exploiting its relationship to heart rate and accelerometer features extracted during submaximal running. To do so, we analyzed data collected from 31 recreational runners (15 men and 16 women) aged 19-26 years who performed a maximal incremental test on a treadmill. During this test, the subjects’ heart rate and acceleration at three locations (the upper back, the lower back and the tibia) were continuously measured. We extracted a wide variety of features from the measurements of the warm-up and the first three stages of the test and employed a data-driven approach to select the most relevant ones. Furthermore, we evaluated the utility of combining different types of features. Empirically, we found that combining heart rate and accelerometer features resulted in the best model with a mean absolute error of 2.33 ml ⋅ kg−1 ⋅ min−1 and a mean absolute percentage error of 4.92%. The model includes four features: gender, body mass, the inverse of the average heart rate and the inverse of the variance of the total tibia acceleration during the warm-up stage of the treadmill test. Our model provides a practical tool for recreational runners in the same age range to estimate their VO2max from submaximal running on a treadmill. It requires two body-worn sensors: a heart rate monitor and an accelerometer positioned on the tibia.status: Published onlin

Relationships between the external and internal training load in professional soccer: what can we learn from machine learning?

PURPOSE: Machine learning may contribute to understanding the relationship between the external load and internal load in professional soccer. Therefore, the relationship between external load indicators (ELIs) and the rating of perceived exertion (RPE) was examined using machine learning techniques on a group and individual level. METHODS: Training data were collected from 38 professional soccer players over 2 seasons. The external load was measured using global positioning system technology and accelerometry. The internal load was obtained using the RPE. Predictive models were constructed using 2 machine learning techniques, artificial neural networks and least absolute shrinkage and selection operator (LASSO) models, and 1 naive baseline method. The predictions were based on a large set of ELIs. Using each technique, 1 group model involving all players and 1 individual model for each player were constructed. These models' performance on predicting the reported RPE values for future training sessions was compared with the naive baseline's performance. RESULTS: Both the artificial neural network and LASSO models outperformed the baseline. In addition, the LASSO model made more accurate predictions for the RPE than did the artificial neural network model. Furthermore, decelerations were identified as important ELIs. Regardless of the applied machine learning technique, the group models resulted in equivalent or better predictions for the reported RPE values than the individual models. CONCLUSIONS: Machine learning techniques may have added value in predicting RPE for future sessions to optimize training design and evaluation. These techniques may also be used in conjunction with expert knowledge to select key ELIs for load monitoring.status: publishe