The American company Amazon has made headlines several times for monitoring its workers in warehouses across Europe and beyond.1 What is new is that a national data protection authority has recently issued a substantial fine of €32 million to the e-commerce giant for breaching several provisions of the General Data Protection Regulation (gdpr) with its surveillance practices. On 27 December 2023, the Commission nationale de l’informatique et des libertés (cnil)—the French Data Protection Authority—determined that Amazon France Logistique infringed on, among others, Articles 6(1)(f) (principle of lawfulness) and 5(1)(c) (data minimization) gdpr by processing some of workers’ data collected by handheld scanner in the distribution centers of Lauwin-Planque and Montélimar.2 Scanners enable employees to perform direct tasks such as picking and scanning items while continuously collecting data on quality of work, productivity, and periods of inactivity.3 According to the company, this data processing is necessary for various purposes, including quality and safety in warehouse management, employee coaching and performance evaluation, and work planning.4 The cnil’s decision centers on data protection law, but its implications reach far beyond into workers’ fundamental right to health and safety at work. As noted in legal literature and policy documents, digital surveillance practices can have a significant impact on workers’ mental health and overall well-being.5 This commentary examines the cnil’s decision through the lens of European occupational health and safety (EU ohs). Its scope is limited to how the French authority has interpreted the data protection principle of lawfulness taking into account the impact of some of Amazon’s monitoring practices on workers’ fundamental right to health and safety.
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In de agro-chemie industrie wordt tegenwoordig een grote hoeveelheid data gegenereerd. De inzet van sensoren voor het monitoren van productieprocessen, het sequensen van gewassen en het karakteriseren van bodemmonsters zijn voorbeelden van activiteiten die veel data opleveren. Tegelijkertijd dalen de kosten voor dataopslag en -verwerking sterk. Bedrijven die hier gebruik van weten te maken, hebben goud in handen.
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Vanuit Fontys Hogescholen wordt veel onderzoek gedaan, met name door onderzoekers van de verschillende lectoraten. Vanzelfsprekend worden er binnen deze onderzoeken veel data verzameld en verwerkt. Fontys onderschrijft het belang van zorgvuldige omgang met onderzoeksdata en vraagt daarom van onderzoekers dat zij hun Research Data Management (RDM) op orde hebben. Denk hierbij aan veilige opslag en duurzame toegankelijkheid van data. Maar ook (open access) publiceren en archiveren van onderzoeksdata maken onderdeel uit van RDM. Hoe je hier als onderzoeker invulling aan geeft kan soms best een zoektocht zijn, mede doordat nog niet iedereen even bekend is met het onderwerp RDM. Met dit boek hopen we onderzoekers binnen Fontys de belangrijkste informatie te bieden die nodig is om goed invulling te geven aan Research Data Management en daarbij ook te wijzen op de ondersteuning die op dit gebied voorhanden is.
Due to societal developments, like the introduction of the ‘civil society’, policy stimulating longer living at home and the separation of housing and care, the housing situation of older citizens is a relevant and pressing issue for housing-, governance- and care organizations. The current situation of living with care already benefits from technological advancement. The wide application of technology especially in care homes brings the emergence of a new source of information that becomes invaluable in order to understand how the smart urban environment affects the health of older people. The goal of this proposal is to develop an approach for designing smart neighborhoods, in order to assist and engage older adults living there. This approach will be applied to a neighborhood in Aalst-Waalre which will be developed into a living lab. The research will involve: (1) Insight into social-spatial factors underlying a smart neighborhood; (2) Identifying governance and organizational context; (3) Identifying needs and preferences of the (future) inhabitant; (4) Matching needs & preferences to potential socio-techno-spatial solutions. A mixed methods approach fusing quantitative and qualitative methods towards understanding the impacts of smart environment will be investigated. After 12 months, employing several concepts of urban computing, such as pattern recognition and predictive modelling , using the focus groups from the different organizations as well as primary end-users, and exploring how physiological data can be embedded in data-driven strategies for the enhancement of active ageing in this neighborhood will result in design solutions and strategies for a more care-friendly neighborhood.
Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
