The goal of this cross-sectional study was to further explore the relationships between motor competence, physical activity, perceived motor competence, physical fitness and weight status in different age categories of Dutch primary school children. Participants were 2068 children aged 4 to 13 years old, divided over 9 age groups. During physical education classes, they completed the 4-Skills Test, a physical activity questionnaire, versions of the Self-Perception Profile for Children, Eurofit test and anthropometry measurements. Results show that all five factors included in the analyses are related to each other and that a tipping point exists at which relations emerge or strengthen. Physical fitness is related to both motor competence and physical activity and these relationships strengthen with age. A relationship between body mass index and the other four factors emerges in middle childhood. Interestingly, at a young age, motor competence and perceived motor competence are weakly related, but neither one of these have a relation with physical activity. In middle childhood, both motor competence and perceived motor competence are related to physical activity. Our findings show that children in late childhood who have higher perceived motor competence are also more physically active, have higher physical fitness, higher motor competence and lower body mass index. Our results indicate that targeting motor competence at a young age might be a feasible way to ensure continued participation in physical activities throughout childhood and adolescence.
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BACKGROUND: Approximately 5%-10% of elementary school children show delayed development of fine motor skills. To address these problems, detection is required. Current assessment tools are time-consuming, require a trained supervisor, and are not motivating for children. Sensor-augmented toys and machine learning have been presented as possible solutions to address this problem.OBJECTIVE: This study examines whether sensor-augmented toys can be used to assess children's fine motor skills. The objectives were to (1) predict the outcome of the fine motor skill part of the Movement Assessment Battery for Children Second Edition (fine MABC-2) and (2) study the influence of the classification model, game, type of data, and level of difficulty of the game on the prediction.METHODS: Children in elementary school (n=95, age 7.8 [SD 0.7] years) performed the fine MABC-2 and played 2 games with a sensor-augmented toy called "Futuro Cube." The game "roadrunner" focused on speed while the game "maze" focused on precision. Each game had several levels of difficulty. While playing, both sensor and game data were collected. Four supervised machine learning classifiers were trained with these data to predict the fine MABC-2 outcome: k-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), and support vector machine (SVM). First, we compared the performances of the games and classifiers. Subsequently, we compared the levels of difficulty and types of data for the classifier and game that performed best on accuracy and F1 score. For all statistical tests, we used α=.05.RESULTS: The highest achieved mean accuracy (0.76) was achieved with the DT classifier that was trained on both sensor and game data obtained from playing the easiest and the hardest level of the roadrunner game. Significant differences in performance were found in the accuracy scores between data obtained from the roadrunner and maze games (DT, P=.03; KNN, P=.01; LR, P=.02; SVM, P=.04). No significant differences in performance were found in the accuracy scores between the best performing classifier and the other 3 classifiers for both the roadrunner game (DT vs KNN, P=.42; DT vs LR, P=.35; DT vs SVM, P=.08) and the maze game (DT vs KNN, P=.15; DT vs LR, P=.62; DT vs SVM, P=.26). The accuracy of only the best performing level of difficulty (combination of the easiest and hardest level) achieved with the DT classifier trained with sensor and game data obtained from the roadrunner game was significantly better than the combination of the easiest and middle level (P=.046).CONCLUSIONS: The results of our study show that sensor-augmented toys can efficiently predict the fine MABC-2 scores for children in elementary school. Selecting the game type (focusing on speed or precision) and data type (sensor or game data) is more important for determining the performance than selecting the machine learning classifier or level of difficulty.
In cognitive science, creative ideas are defined as original and feasible solutions in response to problems. A common proposal is that creative ideas are generated across dedicated cognitive pathways. Only after creative ideas have emerged, they can be enacted to solve the problem. We present an alternative viewpoint, based upon the dynamic systems approach to perception and action, that creative solutions emerge in the act rather than before. Creative actions, thus, are as much a product of individual constraints as they are of the task and environment constraints. Accordingly, we understand creative motor actions as functional movement patterns that are new to the individual and/or group and adapted to satisfy the constraints on the motor problem at hand. We argue that creative motor actions are promoted by practice interventions that promote exploration by manipulating constraints. Exploration enhances variability of functional movement patterns in terms of either coordination or control solutions. At both levels, creative motor actions can emerge from finding new and degenerate adaptive motor solutions. Generally speaking, we anticipate that in most cases, when exposed to variation in constraints, people are not looking for creative motor actions, but discover them while doing an effort to satisfy constraints. For future research, this paper achieves two important aspects: it delineates how adaptive (movement) variability is at the heart of (motor) creativity, and it sets out methodologies toward stimulating adaptive variability.
Paper sludge contains papermaking mineral additives and fibers, which could be reused or recycled, thus enhancing the circularity. One of the promising technologies is the fast pyrolysis of paper sludge, which is capable of recovering > 99 wt.% of the fine minerals in the paper sludge and also affording a bio-liquid. The fine minerals (e.g., ‘circular’ CaCO3) can be reused as filler in consumer products thereby reducing the required primary resources. However, the bio-liquid has a lower quality compared to fossil fuels, and only a limited application, e.g., for heat generation, has been applied. This could be significantly improved by catalytic upgrading of the fast pyrolysis vapor, known as an ex-situ catalytic pyrolysis approach. We have recently found that a high-quality bio-oil (mainly ‘bio-based’ paraffins and low-molecular-weight aromatics, carbon yield of 21%, and HHV of 41.1 MJ kg-1) was produced (Chem. Eng. J., 420 (2021), 129714). Nevertheless, catalyst deactivation occurred after a few hours’ of reaction. As such, catalyst stability and regenerability are of research interest and also of high relevance for industrial implementation. This project aims to study the potential of the add-on catalytic upgrading step to the industrial fast pyrolysis of paper sludge process. One important performance metric for sustainable catalysis in the industry is the level of catalyst consumption (kgcat tprod-1) for catalytic pyrolysis of paper sludge. Another important research topic is to establish the correlation between yield and selectivity of the bio-chemicals and the catalyst characteristics. For this, different types of catalysts (e.g., FCC-type E-Cat) will be tested and several reaction-regeneration cycles will be performed. These studies will determine under which conditions catalytic fast pyrolysis of paper sludge is technically and economically viable.
Europa wil in 2050 volledig klimaatneutraal zijn, en zet in op waterstof als energiedrager die een hoofdrol zal spelen in die transitie. Nederlandse bedrijven die dieselaggregaten produceren zetten voor de volledige reductie van de CO2-emissie van hun producten nu voornamelijk in op het vervangen van fossiele door zogenaamde groene diesel. Recent zijn diverse studies verschenen die aantonen dat de ontwikkeling en inzet van waterstofmotoren, zeker voor toepassingen in wegtransport, zowel technisch als economisch zinvol is. Dergelijk productontwikkeling zou ook bij generatorsets een versnelling van de energietransitie mogelijk maken en bovendien kunnen functioneren als een brugtechnologie tot het ogenblik dat brandstofcellen voldoende goedkoop en duurzaam zijn geworden. Doel van dit project is om de economische en technische haalbaarheid van een dergelijke productontwikkeling in kaart te brengen. Bijzonder is daarbij dat het hierbij gaat om een retrofit van bestaande motoren die massaal geproduceerd worden voor automotive doeleinden. Omdat voorlopig gebruik zal worden gemaakt van componenten die ontwikkeld zijn voor de huidige aardgasmotoren voorziet het project ook in versneld duurproefonderzoek van deze componenten. In dit project wordt samengewerkt tussen Fontys Hogeschool Engineering, NPS Diesel B.V., H2Trac B.V., Prins Autogassystemen B.V. en TNO.
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.
