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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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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Thermal comfort -the state of mind, which expresses satisfaction with the thermal environment- is an important aspect of the building design process as modern man spends most of the day indoors. This paper reviews the developments in indoor thermal comfort research and practice since the second half of the 1990s, and groups these developments around two main themes; (i) thermal comfort models and standards, and (ii) advances in computerization. Within the first theme, the PMV-model (Predicted Mean Vote), created by Fanger in the late 1960s is discussed in the light of the emergence of models of adaptive thermal comfort. The adaptive models are based on adaptive opportunities of occupants and are related to options of personal control of the indoor climate and psychology and performance. Both models have been considered in the latest round of thermal comfort standard revisions. The second theme focuses on the ever increasing role played by computerization in thermal comfort research and practice, including sophisticated multi-segmental modeling and building performance simulation, transient thermal conditions and interactions, thermal manikins.
