Physical activity is crucial in human life, whether in everyday activities or elite sports. It is important to maintain or improve physical performance, which depends on various factors such as the amount of physical activity, the capability, and the capacity of the individual. In daily life, it is significant to be physically active to maintain good health, intense exercise is not necessary, as simple daily activities contribute enough. In sports, it is essential to balance capacity, workload, and recovery to prevent performance decline or injury.With the introduction of wearable technology, it has become easier to monitor and analyse physical activity and performance data in daily life and sports. However, extracting personalised insights and predictions from the vast and complex data available is still a challenge.The study identified four main problems in data analytics related to physical activity and performance: limited personalised prediction due to data constraints, vast data complexity, need for sensitive performance measures, overly simplified models, and missing influential variables. We proposed end investigated potential solutions for each issue. These solutions involve leveraging personalised data from wearables, combining sensitive performance measures with various machine learning algorithms, incorporating causal modelling, and addressing the absence of influential variables in the data.Personalised data, machine learning, sensitive performance measures, advanced statistics, and causal modelling can help bridge the data analytics gap in understanding physical activity and performance. The research findings pave the way for more informed interventions and provide a foundation for future studies to further reduce this gap.
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The paper explores the effectiveness of automated clustering in personalized applications based on data characteristics. It evaluates three clustering algorithms with various cluster numbers and subsets of characteristics. The study compares the accuracy of models in different clusters against original results and examines the algorithmic approaches and characteristic selections for optimal clustering performance. The research concludes that the proposed method aids in selecting appropriate clustering strategies and relevant characteristics for datasets. These insights may also guide further research on coaching approaches within applications.
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Deze publicatie geeft gerichte theoretische en praktische informatie ten behoeve van respectievelijk de gebruikers van de diverse machines en gereedschappen welke bij het omvormproces (dieptrekken, kraagtrekken, strekken, alsmede buigen en scheiden) worden gebruikt, geïnteresseerden in de betreffende processen, technische cursussen en opleidingen. De inhoud van deze publicatie behandelt de belangrijkste machines en gereedschappen, alsmede aanvullende informatie welke bij het vormgeven van dunne plaat van belang zijn. In de voorlichtingspublicaties VM 110 "Dieptrekken", VM 113 "Buigen" alsmede VM 114 "Scheiden" vindt u gegevens m.b.t. de diverse omvormprocessen en in VM 111 "Materialen" worden de hierbij gebruikte materialen behandeld. Deze voorlichtingspublicatie is een update van de in 2000 verschenen eerste druk, welke toentertijd is samengesteld door de werkgroep "Dieptrekken van dunne plaat, staal, aluminium". In het kader van een updateproject heeft het NIMR, inmiddels M2i (Materials innovation institute) geheten, geld ter beschikking gesteld om deze publicaties te vernieuwen en aan te passen aan de huidige stand der techniek.
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