In sports, inertial measurement units are often used to measure the orientation of human body segments. A Madgwick (MW) filter can be used to obtain accurate inertial measurement unit (IMU) orientation estimates. This filter combines two different orientation estimates by applying a correction of the (1) gyroscope-based estimate in the direction of the (2) earth frame-based estimate. However, in sports situations that are characterized by relatively large linear accelerations and/or close magnetic sources, such as wheelchair sports, obtaining accurate IMU orientation estimates is challenging. In these situations, applying the MW filter in the regular way, i.e., with the same magnitude of correction at all time frames, may lead to estimation errors. Therefore, in this study, the MW filter was extended with machine learning to distinguish instances in which a small correction magnitude is beneficial from instances in which a large correction magnitude is beneficial, to eventually arrive at accurate body segment orientations in IMU-challenging sports situations. A machine learning algorithm was trained to make this distinction based on raw IMU data. Experiments on wheelchair sports were performed to assess the validity of the extended MW filter, and to compare the extended MW filter with the original MW filter based on comparisons with a motion capture-based reference system. Results indicate that the extended MW filter performs better than the original MW filter in assessing instantaneous trunk inclination (7.6 vs. 11.7◦ root-mean-squared error, RMSE), especially during the dynamic, IMU-challenging situations with moving athlete and wheelchair. Improvements of up to 45% RMSE were obtained for the extended MW filter compared with the original MW filter. To conclude, the machine learning-based extended MW filter has an acceptable accuracy and performs better than the original MW filter for the assessment of body segment orientation in IMU-challenging sports situations.
The objective of this study is to contribute towards understanding how welfare and justice discourses become apparent in institutional conversations where social workers involved in child protection have dual professional identities: that of helper and of gatekeeper. In this article we analyse a specific conversational practice in a particular child protection context: social workers asking questions about hypothetical situations in interviews with prospective adoptive parents. We show the nature of these questions in face-to-face interactions between social workers and prospective adoptive parents. In addition, we also analyse how the social workers manage to integrate aspects of testing the capabilities of the prospective adoptive parents while, at the same time, also helping them to become even better prepared parents. Using the method of conversation analysis makes it possible to analyse how the social workers are doing being a gatekeeper and/or helper without spelling that out.
When studying the acquisition of social-communicative competence, it is important to take students' individual learning theories into account. Increased insight into the role ILTs play can be of help in improving social work education.
Nederland kent ongeveer 220.000 bedrijfsongevallen per jaar (met 60 mensen die overlijden). Vandaar dat elke werkgever verplicht is om bedrijfshulpverlening (BHV) te organiseren, waaronder BHV-trainingen. Desondanks brengt slechts een-derde van alle bedrijven de arbeidsrisico’s in kaart via een Risico-Inventarisatie & Evaluatie (RI&E) en blijft het aandeel werknemers met een arbeidsongeval hoog. Daarom wordt er continu geïnnoveerd om BHV-trainingen te optimaliseren, o.a. door middel van Virtual Reality (VR). VR is niet nieuw, maar is wel doorontwikkeld en betaalbaarder geworden. VR biedt de mogelijkheid om veilige realistische BHV-noodsimulaties te ontwikkelen waarbij de cursist het gevoel heeft daar echt te zijn. Ondanks de toename in VR-BHV-trainingen, is er weinig onderzoek gedaan naar het effect van VR in BHV-trainingen en zijn resultaten tegenstrijdig. Daarnaast zijn er nieuwe technologische ontwikkelingen die het mogelijk maken om kijkgedrag te meten in VR m.b.v. Eye-Tracking. Tijdens een BHV-training kan met Eye-Tracking gemeten worden hoe een instructie wordt opgevolgd, of cursisten worden afgeleid en belangrijke elementen (gevaar en oplossingen) waarnemen tijdens de simulatie. Echter, een BHV-training met VR en Eye-Tracking (interacties) bestaat niet. In dit project wordt een prototype ontwikkeld waarin Eye-Tracking wordt verwerkt in een 2021 ontwikkelde VR-BHV-training, waarin noodsituaties zoals een kantoorbrand worden gesimuleerd (de BHVR-toepassing). Door middel van een experiment zal het prototype getest worden om zo voor een deel de vraag te beantwoorden in hoeverre en op welke manier Eye-Tracking in VR een meerwaarde biedt voor (RI&E) BHV-trainingen. Dit project sluit daarmee aan op het missie-gedreven innovatiebeleid ‘De Veiligheidsprofessional’ en helpt het MKB dat vaak middelen en kennis ontbreekt voor onderzoek naar effectiviteit rondom innovatieve-technologieën in educatie/training. Het project levert onder meer een prototype op, een productie-rapport en onderzoeks-artikel, en staat open voor nieuwe deelnemers bij het schrijven van een grotere aanvraag rondom de toepassing en effect van VR en Eye-Tracking in BHV-trainingen.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
Electronic Sports (esports) is a form of digital entertainment, referred to as "an organised and competitive approach to playing computer games". Its popularity is growing rapidly as a result of an increased prevalence of online gaming, accessibility to technology and access to elite competition.Esports teams are always looking to improve their performance, but with fast-paced interaction, it can be difficult to establish where and how performance can be improved. While qualitative methods are commonly employed and effective, their widespread use provides little differentiation among competitors and struggles with pinpointing specific issues during fast interactions. This is where recent developments in both wearable sensor technology and machine learning can offer a solution. They enable a deep dive into player reactions and strategies, offering insights that surpass traditional qualitative coaching techniquesBy combining insights from gameplay data, team communication data, physiological measurements, and visual tracking, this project aims to develop comprehensive tools that coaches and players can use to gain insight into the performance of individual players and teams, thereby aiming to improve competitive outcomes. Societal IssueAt a societal level, the project aims to revolutionize esports coaching and performance analysis, providing teams with a multi-faceted view of their gameplay. The success of this project could lead to widespread adoption of similar technologies in other competitive fields. At a scientific level, the project could be the starting point for establishing and maintaining further collaboration within the Dutch esports research domain. It will enhance the contribution from Dutch universities to esports research and foster discussions on optimizing coaching and performance analytics. In addition, the study into capturing and analysing gameplay and player data can help deepen our understanding into the intricacies and complexities of teamwork and team performance in high-paced situations/environments. Collaborating partnersTilburg University, Breda Guardians.
