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The prediction of the running injuries based on selfreported training data on load is difficult. At present, coaches and researchers have no validated system to predict if a runner has an increased risk of injuries. We aim to develop an algorithm to predict the increase of the risk of a runner to sustain an injury. As a first step Self-reported data on training parameters and injuries from high-level runners (duration=37 weeks, n=23, male=16, female=7) were used to identify the most predictive variables for injuries, and train a machine learning tree algorithm to predict an injury. The model was validated by splitting the data in training and a test set. The 10 most important variables were identified from 85 possible variables using the Random Forest algorithm. To predict at an earliest stage, so the runner or the coach is able to intervene, the variables were classified by time to build tree algorithms up to 7 weeks before the occurrence of an injury. By building machine learning algorithms using existing self-reported training data can enable prospective identification of high-level runners who are likely to develop an injury. Only the established prediction model needs to be verified as correct.
Background/aim We aimed to investigate the magnitude and characteristics of injuries and illnesses in Dutch physical education teacher education (PETE) students.Methods During the first 21 weeks of the academic year, 245 first-year students registered their health problems online using the Oslo Sports Trauma Research Centre (OSTRC) Questionnaire on Health Problems.Results A total of 276 injuries, 140 illnesses and 69 unclassified health problems were reported. We found an injury incidence rate of 11.7 injuries per 1000 hours (95% CI 10.4 to 13.2). Injury characteristics were: 42% overuse injuries, 62% causing absence from sports (median injury time loss=2 days) and 64% reinjuries. Most injuries were located at the knee, lower leg (anterior) and ankle. The duration of the illnesses was short (<1 week).Summary and conclusions We implemented a new registration method in the PETE academic programme. The results show that the risk for health problems is high for PETE students. Prevention is necessary, and to decrease injuries prevention programmes should focus on the lower extremities.
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
Ballet en moderne dans zijn een vorm van topsport. De druk op dansers is enorm. Lange en intensieve werkdagen, veel reizen en verschillende werkplekken maken het lastig om lichaam en geest goed te verzorgen. Hierdoor liggen blessures en mentale klachten op de loer. Nederlandse dansgezelschappen willen meer aandacht gaan besteden aan preventieve maatregelen om fysieke en mentale problemen bij hun dansers te voorkomen. Het ontbreekt hen echter aan kennis en kunde om dit innovatieve vraagstuk op te kunnen pakken. Het Nationale Ballet en het Scapino Ballet hebben het lectoraat Performing Arts Medicine van Codarts (Hogeschool voor de Kunsten Rotterdam) benaderd om antwoord te krijgen op de vraag hoe dansers op de hoogste podia, op gezonde wijze, hun beste performance kunnen laten zien. Gezamenlijk is deze praktijkvraag omgevormd naar drie onderzoeksdoelstellingen: 1. Opstellen van meetinstrumenten om de fysieke en mentale gezondheid van dansers te screenen en te monitoren; 2. Ontwerpen van een web-based systeem dat automatisch en real-time informatie uit de ontwikkelde meetinstrumenten kan inlezen, analyseren en interpreteren; 3. Ontwikkelen van een Fit to Perform protocol dat aanbevelingen geeft ten aanzien van het verbeteren van de fysieke en mentale gesteldheid van de danser. Het consortium bestaat uit de volgende organisaties: - Praktijkgerichte onderzoeksinstellingen: Codarts Rotterdam en Hogeschool van Amsterdam; - Universiteiten: ErasmusMC, Technische Universiteit Eindhoven en Vrije Universiteit Amsterdam; - Praktijkinstellingen: Het Nationale Ballet en het Scapino Ballet; - Overige instellingen: het Nederlands Paramedisch Instituut (NPi) en het Nationale Centrum Performing Arts (NCPA). Bij de samenstelling van het consortium is gekozen voor een goede mix tussen praktijkorganisaties, onderzoeksinstituten en onderwijsinstellingen. Daarnaast is er sprake van cross-sectorale samenwerking doordat kennis vanuit de podiumkunsten, sport, gezondheidszorg, onderwijs en technologie met elkaar verbonden wordt.
Despite the recognized benefits of running for promoting overall health, its widespread adoption faces a significant challenge due to high injury rates. In 2022, runners reported 660,000 injuries, constituting 13% of the total 5.1 million sports-related injuries in the Netherlands. This translates to a disturbing average of 5.5 injuries per 1,000 hours of running, significantly higher than other sports such as fitness (1.5 injuries per 1,000 hours). Moreover, running serves as the foundation of locomotion in various sports. This emphasizes the need for targeted injury prevention strategies and rehabilitation measures. Recognizing this social issue, wearable technologies have the potential to improve motor learning, reduce injury risks, and optimize overall running performance. However, unlocking their full potential requires a nuanced understanding of the information conveyed to runners. To address this, a collaborative project merges Movella’s motion capture technology with Saxion’s expertise in e-textiles and user-centered design. The result is the development of a smart garment with accurate motion capture technology and personalized haptic feedback. By integrating both sensor and actuator technology, feedback can be provided to communicate effective risks and intuitive directional information from a user-centered perspective, leaving visual and auditory cues available for other tasks. This exploratory project aims to prioritize wearability by focusing on robust sensor and actuator fixation, a suitable vibration intensity and responsiveness of the system. The developed prototype is used to identify appropriate body locations for vibrotactile stimulation, refine running styles and to design effective vibration patterns with the overarching objective to promote motor learning and reduce the risk of injuries. Ultimately, this collaboration aims to drive innovation in sports and health technology across different athletic disciplines and rehabilitation settings.