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.
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Voor het meten van de mobiliteit bij geriatrische patiënten en bij patiënten met evenwichtsstoornissen is in 1986 door Mathias de Timed Up and Go test (TUG) ontwikkeld. Deze oorspronkelijk Engelstalige versie van de test werd in 2000 in het Nederlands vertaald door De Jong. Het meetinstrument is zowel evaluatief als inventariserend en wordt aanbevolen in de KNGF-richtlijnen ‘Osteoporose’, ‘Beroerte’ en ‘Ziekte van Parkinson’.
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In this study we measured the performance times on the Wheelchair Mobility Performance (WMP) test during different test conditions to see if the performance times changed when wheelchair settings were changed. The overall performance time on the WMP test increased when the tire pressure was reduced and also when extra mass was attached to the wheelchair. It can be concluded that the WMP test is sensitive to changes in wheelchair settings. It is recommended to use this field-based test in further research to investigate the effect of wheelchair settings on mobility performance time. Objective: The Wheelchair Mobility Performance (WMP) test is a reliable and valid measure to assess mobility performance in wheelchair basketball. The aim of this study was to examine the sensitivity to change of the WMP test by manipulating wheelchair configurations. Methods: Sixteen wheelchair basketball players performed the WMP test 3 times in their own wheelchair: (i) without adjustments (“control condition”); (ii) with 10 kg additional mass (“weighted condition”); and (iii) with 50% reduced tyre pressure (“tyre condition”). The outcome measure was time (s). If paired t-tests were significant (p < 0.05) and differences between conditions were larger than the standard error of measurement, the effect sizes (ES) were used to evaluate the sensitivity to change. ES values ≥0.2 were regarded as sensitive to change. Results: The overall performance times for the manipulations were significantly higher than the control condition, with mean differences of 4.40 s (weight – control, ES = 0.44) and 2.81 s (tyre – control, ES = 0.27). The overall performance time on the WMP test was judged as sensitive to change. For 8 of the 15 separate tasks on the WMP test, the tasks were judged as sensitive to change for at least one of the manipulations. Conclusion: The WMP test can detect change in mobility performance when wheelchair configurations are manipulated. https://www.medicaljournals.se/jrm/content/html/10.2340/16501977-2341
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Due to the existing pressure for a more rational use of the water, many public managers and industries have to re-think/adapt their processes towards a more circular approach. Such pressure is even more critical in the Rio Doce region, Minas Gerais, due to the large environmental accident occurred in 2015. Cenibra (pulp mill) is an example of such industries due to the fact that it is situated in the river basin and that it has a water demanding process. The current proposal is meant as an academic and engineering study to propose possible solutions to decrease the total water consumption of the mill and, thus, decrease the total stress on the Rio Doce basin. The work will be divided in three working packages, namely: (i) evaluation (modelling) of the mill process and water balance (ii) application and operation of a pilot scale wastewater treatment plant (iii) analysis of the impacts caused by the improvement of the process. The second work package will also be conducted (in parallel) with a lab scale setup in The Netherlands to allow fast adjustments and broaden evaluation of the setup/process performance. The actions will focus on reducing the mill total water consumption in 20%.
In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
The focus of the research is 'Automated Analysis of Human Performance Data'. The three interconnected main components are (i)Human Performance (ii) Monitoring Human Performance and (iii) Automated Data Analysis . Human Performance is both the process and result of the person interacting with context to engage in tasks, whereas the performance range is determined by the interaction between the person and the context. Cheap and reliable wearable sensors allow for gathering large amounts of data, which is very useful for understanding, and possibly predicting, the performance of the user. Given the amount of data generated by such sensors, manual analysis becomes infeasible; tools should be devised for performing automated analysis looking for patterns, features, and anomalies. Such tools can help transform wearable sensors into reliable high resolution devices and help experts analyse wearable sensor data in the context of human performance, and use it for diagnosis and intervention purposes. Shyr and Spisic describe Automated Data Analysis as follows: Automated data analysis provides a systematic process of inspecting, cleaning, transforming, and modelling data with the goal of discovering useful information, suggesting conclusions and supporting decision making for further analysis. Their philosophy is to do the tedious part of the work automatically, and allow experts to focus on performing their research and applying their domain knowledge. However, automated data analysis means that the system has to teach itself to interpret interim results and do iterations. Knuth stated: Science is knowledge which we understand so well that we can teach it to a computer; and if we don't fully understand something, it is an art to deal with it.[Knuth, 1974]. The knowledge on Human Performance and its Monitoring is to be 'taught' to the system. To be able to construct automated analysis systems, an overview of the essential processes and components of these systems is needed.Knuth Since the notion of an algorithm or a computer program provides us with an extremely useful test for the depth of our knowledge about any given subject, the process of going from an art to a science means that we learn how to automate something.
