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.
LINK
In this study, we investigated the effects of wearing a police uniform and gear on officers’ performance during the Physical Competence Test (PCT) of the Dutch National Police. In a counterbalanced within-subjects design, twenty-seven police officers performed the PCT twice, once wearing sportswear and once wearing a police uniform. The results showed clear indications that wearing a police uniform influenced the performance on the PCT. Participants were on average 14 seconds slower in a police uniform than in sportswear. Furthermore, performing the test in uniform was accompanied by higher RPE-scores and total physiological load. It seems that wearing a police uniform during the test diminishes the discrepancy between physical fitness needed to pass the simulated police tasks in the PCT and the job-specific physical fitness that is required during daily police work. This suggests that wearing a police uniform during the test will increase the representativeness of the testing environment for the work field.
This paper investigates whether encouraging children to become more physically active in their everyday life affects their primary school performance. We use data from a field quasi‐experiment called the Active Living Program, which aimed to increase active modes of transportation to school and active play among 8‐ to 12‐year‐olds living in low socioeconomic status (SES) areas in the Netherlands. Difference‐in‐differences estimations reveal that while the interventions increase time spent on physical activity during school hours, they negatively affect school performance, especially among the worst‐performing students. Further analyses reveal that increased restlessness during instruction time is a potential mechanism for this negative effect. Our results suggest that the commonly found positive effects of exercising or participating in sports on educational outcomes may not be generalizable to physical activity in everyday life. Policymakers and educators who seek to increase physical activity in everyday life need to weigh the health and well‐being benefits against the probability of increasing inequality in school performance.
With increasing penetration rates of driver assistance systems in road vehicles, powerful sensing and processing solutions enable further automation of on-road as well as off-road vehicles. In this maturing environment, SMEs are stepping in and education needs to align with this trend. By the input of student teams, HAN developed a first prototype robot platform to test automated vehicle technology in dynamic road scenarios that include VRUs (Vulnerable Road Users). These robot platforms can make complex manoeuvres while carrying dummies of typical VRUs, such as pedestrians and bicyclists. This is used to test the ability of automated vehicles to detect VRUs in realistic traffic scenarios and exhibit safe behaviour in environments that include VRUs, on public roads as well as in restricted areas. Commercially available VRU-robot platforms are conforming to standards, making them inflexible with respect to VRU-dummy design, and pricewise they are far out of reach for SMEs, education and research. CORDS-VTS aims to create a first, open version of an integrated solution to physically emulate traffic scenarios including VRUs. While analysing desired applications and scenarios, the consortium partners will define prioritized requirements (e.g. robot platform performance, dummy types and behaviour, desired software functionality, etc.). Multiple robots and dummies will be created and practically integrated and demonstrated in a multi-VRU scenario. The aim is to create a flexible, upgradeable solution, published fully in open source: The hardware (robot platform and dummies) will be published as well-documented DIY (do-it-yourself) projects and the accompanying software will be published as open-source projects. With the CORDS-VTS solution, SME companies, researchers and educators can test vehicle automation technology at a reachable price point and with the necessary flexibility, enabling higher innovation rates.
Today, embedded devices such as banking/transportation cards, car keys, and mobile phones use cryptographic techniques to protect personal information and communication. Such devices are increasingly becoming the targets of attacks trying to capture the underlying secret information, e.g., cryptographic keys. Attacks not targeting the cryptographic algorithm but its implementation are especially devastating and the best-known examples are so-called side-channel and fault injection attacks. Such attacks, often jointly coined as physical (implementation) attacks, are difficult to preclude and if the key (or other data) is recovered the device is useless. To mitigate such attacks, security evaluators use the same techniques as attackers and look for possible weaknesses in order to “fix” them before deployment. Unfortunately, the attackers’ resourcefulness on the one hand and usually a short amount of time the security evaluators have (and human errors factor) on the other hand, makes this not a fair race. Consequently, researchers are looking into possible ways of making security evaluations more reliable and faster. To that end, machine learning techniques showed to be a viable candidate although the challenge is far from solved. Our project aims at the development of automatic frameworks able to assess various potential side-channel and fault injection threats coming from diverse sources. Such systems will enable security evaluators, and above all companies producing chips for security applications, an option to find the potential weaknesses early and to assess the trade-off between making the product more secure versus making the product more implementation-friendly. To this end, we plan to use machine learning techniques coupled with novel techniques not explored before for side-channel and fault analysis. In addition, we will design new techniques specially tailored to improve the performance of this evaluation process. Our research fills the gap between what is known in academia on physical attacks and what is needed in the industry to prevent such attacks. In the end, once our frameworks become operational, they could be also a useful tool for mitigating other types of threats like ransomware or rootkits.
Eggshell particles as bio-ceramic in sustainable bioplastic engineering – ESP-BIOPACK Plastics make our lives easier in many ways. However, if they are not properly disposed of, they end up in the environment. Recently, biodegradable biopolymers, such as polylactic acid (PLA) and polyhydroxy alkanoates (PHAs), have moved towards alternatives for applications such as sustainable packaging. The major limitations of these biopolymers are the high cost, which is due to the high cost of the starting materials and the small volumes, and the poor thermal and mechanical properties such as limited processability and low impact resistance. Attempts to modify PHAs have been researched in many ways, such as blending various biodegradable polymers or mixing inorganic mineral fillers. Eggshell (10 million tons per year by 2030) is a natural bio-ceramic mineral with a unique chemical composition of calcium carbonate (>95% calcite). So far it has been regarded as a zero-value waste product, but it could be a great opportunity as raw material to reduce the cost of biopolymers and to improve properties, including the decomposition process at the end-of-life. In this project, we aim to develop eggshell particles that serve as bio-fillers in biopolymers to lower the cost of the product, to improve mechanical properties and to facilitate the validation of end-of-life routes, therefore, economically enhance the wide applications of such. The developed bioplastic packaging materials will be applied in SME partner EGGXPERT’s cosmetics line but also in other packaging applications, such as e.g. biodegradable coffee capsules. To be able to realize the proposed idea, the partnership between Chemelot Innovation and Learning Labs (CHILL), EGGXPERT B.V. and the Research Centre Material Sciences of Zuyd University of Applied Sciences is needed to research the physical, mechanical and end-of-life influences of eggshell particles (ESP) in biopolymers such as PLA and PHA and optimize their performance.
