Athlete development depends on many factors that need to be balanced by the coach. The amount of data collected grows with the development of sensor technology. To make data-informed decisions for training prescription of their athletes, coaches could be supported by feedback through a coach dashboard. The aim of this paper is to describe the design of a coach dashboard based on scientific knowledge, user requirements, and (sensor) data to support decision making of coaches for athlete development in cyclic sports. The design process involved collaboration with coaches, embedded scientists, researchers, and IT professionals. A classic design thinking process was used to structure the research activities in five phases: empathise, define, ideate, prototype, and test phases. To understand the user requirements of coaches, a survey (n = 38), interviews (n = 8) and focus-group sessions (n = 4) were held. Design principles were adopted into mock-ups, prototypes, and the final coach dashboard. Designing a coach dashboard using the co-operative research design helped to gain deep insights into the specific user requirements of coaches in their daily training practice. Integrating these requirements, scientific knowledge, and functionalities in the final coach dashboard allows the coach to make data-informed decisions on training prescription and optimise athlete development.
Using the latest industrial robot technology, the collaborative robot (cobot), industrial manufacturers work towards high-mix low-volume production systems that could satisfy a diversifying customer demand. As the utilization of the cobot’s potential depends on the dynamic interaction with operators, one would expect HR professionals to play a central role in this implementation process. However, cobot-related literature is unanimous: HR is not involved. This is in line with the results of our study in 2019 on seventeen cobot experiments in Dutch industrial manufacturing companies. To explore what human cobot collaboration emerges when engineers and line managers take the lead in their design, we revisited the data from our previous interview study (N=53). HR was absent in all implementations. We found that line managers and engineers prepared operators for rigid human-cobot collaborations that were aimed at getting the cobot to work, enhancing production efficiency and handling a few batches of mass-produced goods (low-mix, high-volume). Furthermore, the collaborations all showed signs of being difficult to sustain over time and posed a direct threat to operators’ well-being. To protect operators’ future of work and build towards interdependent human-cobot collaboration suitable for high-mix low-volume production, we propose an approach in which operators themselves, and HR too, are much more involved in the cobot implementation process. Operators should be allowed and supported to design, program, operate, and repair as much of their human-cobot workstations themselves as possible. To support this, HR has to familiarize itself with the cobot technology, secure operators’ decision latitude, facilitate the required support, and become the work design expert that helps operators co-design sustainable cobot applications that optimally utilize the strengths of both man and machine.
MULTIFILE
As every new generation of civil aircraft creates more on-wing data and fleets gradually become more connected with the ground, an increased number of opportunities can be identified for more effective Maintenance, Repair and Overhaul (MRO) operations. Data are becoming a valuable asset for aircraft operators. Sensors measure and record thousands of parameters in increased sampling rates. However, data do not serve any purpose per se. It is the analysis that unleashes their value. Data analytics methods can be simple, making use of visualizations, or more complex, with the use of sophisticated statistics and Artificial Intelligence algorithms. Every problem needs to be approached with the most suitable and less complex method. In MRO operations, two major categories of on-wing data analytics problems can be identified. The first one requires the identification of patterns, which enable the classification and optimization of different maintenance and overhaul processes. The second category of problems requires the identification of rare events, such as the unexpected failure of parts. This cluster of problems relies on the detection of meaningful outliers in large data sets. Different Machine Learning methods can be suggested here, such as Isolation Forest and Logistic Regression. In general, the use of data analytics for maintenance or failure prediction is a scientific field with a great potentiality. Due to its complex nature, the opportunities for aviation Data Analytics in MRO operations are numerous. As MRO services focus increasingly in long term contracts, maintenance organizations with the right forecasting methods will have an advantage. Data accessibility and data quality are two key-factors. At the same time, numerous technical developments related to data transfer and data processing can be promising for the future.
Het aantal dierlijke graverijen in fysieke infrastructuren, waaronder waterkeringen, spoordijken en autowegen, neemt de laatste jaren hard toe. Dit komt door exponentiële groei van de bever die in Nederland een beschermde status heeft. Waterschappen geven aan dat de inspectie en detectie van graverijen door bevers geen gemakkelijke opgave is. De gevolgen voor de veiligheid van primaire waterkeringen en spoordijken kunnen aanzienlijk zijn. Om grip te krijgen op graverijen, zijn tot op heden verschillende aanpakken gehanteerd van lopen door watergangen in waadpakken met prikstokken t/m de inzet van GPR, camera- en sonartechnologie alsook getrainde speurhonden. Tot op heden is er nog geen oplossing gevonden voor ongewenste graverijen door bevers. Met dit onderzoeksproject wordt nieuwe technologische kennis ontwikkeld en toegevoegd aan de state-of-the-art op het gebied van detectie van beveractiviteiten (graverijen). In dit project wordt een robot platform (hardware/software) ontwikkeld dat beverschades aan kritieke publieke infrastructuren kan detecteren en monitoren. Hiervoor zijn robuuste technologieën nodig die gangenstelsels/kamers kunnen waarnemen (perceptie), zelfstandig in kaart kunnen brengen (autonome navigatie). Daarnaast moeten operators (veldwerkers) het robot platform eenvoudig kunnen toepassen in hun dagelijkse gebruik (mens-robot interactie). Het consortium bestaat uit publiek partijen (waterschappen, Rijkswaterstaat, provincies), prorail technologieontwikkelaars en dienstleveranciers (MKBs, ander privaat partijen), onderzoeksgroepen van Saxion (lectoraten SMART en TCI), opleidingen en overkoepelende innovatie boosters. Zij zetten kennis en capaciteit in om antwoord te geven op de centrale onderzoeksvraag: “Welke bestaande navigatie- en perceptietechnologieën kunnen binnen een periode van 2 jaar worden doorontwikkeld tot de realisatie en inzet van een gebruiksvriendelijk beverbeheer robotplatform waarmee ongewenste beveractiviteiten vroegtijdig kunnen worden gedetecteerd en herstelmaatregelen effectief kunnen worden ingezet?” Opbrengsten van het project dragen bij aan duurzaam beverbeheer, preventieve detectie en kosteneffectieve inzet van maatregelen die nadien op basis van de verschillende detectiemethoden kunnen worden ontwikkeld. Daarnaast vindt borging van (technologische) kennis plaats in alle deelnemende partijen en opleidingen.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
During the coronavirus pandemic, the use of eHealth tools became increasingly demanded by patients and encouraged by the Dutch government. Yet, HBO health professionals demand clarity on what they can do, must do, and cannot do with the patients’ data when using digital healthcare provision and support. They often perceive the EU GDPR and its national application as obstacles to the use of eHealth due to strict health data processing requirements. They highlight the difficulty of keeping up with the changing rules and understanding how to apply them. Dutch initiatives to clarify the eHealth rules include the 2021 proposal of the wet Elektronische Gegevensuitwisseling in de Zorg and the establishment of eHealth information and communication platforms for healthcare practitioners. The research explores whether these initiatives serve the needs of HBO health professionals. The following questions will be explored: - Do the currently applicable rules and the proposed wet Elektronische Gegevensuitwisseling in de Zorg clarify what HBO health practitioners can do, must do, and cannot do with patients’ data? - Does the proposed wet Elektronische Gegevensuitwisseling in de Zorg provide better clarity on the stakeholders who may access patients’ data? Does it ensure appropriate safeguards against the unauthorized use of such data? - Does the proposed wet Elektronische Gegevensuitwisseling in de Zorg clarify the EU GDPR requirements for HBO health professionals? - Do the eHealth information and communication platforms set up for healthcare professionals provide the information that HBO professionals need on data protection and privacy requirements stemming from the EU GDPR and from national law? How could such platforms be better adjusted to the HBO professionals’ information and communication needs? Methodology: Practice-oriented legal research, semi-structured interviews and focus group discussions will be conducted. Results will be translated to solutions for HBO health professionals.
