visuAAL - Privacy-Aware and Acceptable Video-Based Technologies and Services for Active and Assisted Living
This deliverable explores how video-based technologies used in healthcare and Ambient Assisted Living (AAL) can be designed and implemented in a way that respects users’ privacy and gains their acceptance. It reviews the benefits and barriers of adopting such technologies, with a particular focus on how privacy concerns shape users’ willingness to accept video monitoring, especially in private spaces like bedrooms or bathrooms. Drawing on a mix of qualitative and quantitative user studies, the report highlights the need to involve older adults, caregivers, and healthcare professionals in the design process. It stresses that successful adoption depends not only on technical effectiveness but also on social and psychological factors, including trust and control. The document also offers best practice recommendations, including avoiding ageist assumptions, balancing exploratory and confirmatory research, offering realistic technology trials, and fostering collaboration across disciplines. Overall, it calls for user-centred, privacy-aware design as essential for acceptable and responsible innovation in video-based AAL technologies.
This deliverable presents an “acceptance cartography” for video-based Ambient Assisted Living (AAL) technologies, mapping the factors that influence whether people accept or reject these systems. It explores how artificial intelligence (AI) supports AAL tools, particularly video-based ones used for monitoring and supporting older adults and people with disabilities at home. While these technologies can improve safety and independence, their acceptance depends on various conditions. Key influences include perceived usefulness, ease of use, trust in the technology, and privacy concerns. The report stresses that acceptance is not a simple yes/no decision but a complex process shaped by personal, technological, and contextual factors. It highlights the importance of users’ previous experiences with care, their technical confidence, living situation, and health status. Privacy concerns are especially significant, as many people are uncomfortable with constant video monitoring, particularly in private areas. Trust and transparency about how data is collected and used are essential. The report concludes with best-practice recommendations, including providing user control over the system, ensuring transparency, and offering flexible, privacy-sensitive design options to support user confidence and uptake.
This whitepaper aims to explore and present the current legal framework of AAL technologies in a systematic manner. It does so by mapping out legal issues in multiple relevant legal domains, including: (1) general product safety regulations (2) medical device regulation; (3) data protection; (4) cybersecurity; (5) competition law; (6) consumer protection; (7) contract law; (8) criminal law. Gaps, uncertainties, and contradictions in these legal domains are highlighted and discussed in the context of AAL technologies.
This deliverable provides legal and ethical guidelines for responsible research and innovation in monitoring technologies and Ambient Assisted Living (AAL) systems. It identifies key regulatory challenges across eight legal areas: product safety, medical device regulations, cybersecurity, competition law, consumer law, contract law, criminal law, and data protection. The report argues that current EU legal frameworks are fragmented and often fail to fully address the hybrid nature of AAL systems, which combine hardware, software, and services. It calls for legal updates that better define liability, ensure cybersecurity, and balance innovation with user safety and rights. The guidelines are based on three years of interdisciplinary research and suggest future-proof legal reforms, such as extending strict liability to software and AI components, introducing international cybersecurity standards, and ensuring clear responsibilities across stakeholders. The document highlights the need for a coordinated, integrated approach to regulation that supports both innovation and the rights of older adults and users of AAL technologies.
This deliverable reports on research into how video-based camera systems can help older adults manage multiple chronic health conditions at home. It focuses on three main areas: how visual systems support self-management (ESR 7), how behaviour change strategies can improve acceptance (ESR 8), and how to address privacy and ethical concerns. The first part shows that camera systems can help older adults with tasks such as physical activity, medication, and rehabilitation, offering benefits especially when they are easy to use and deliver clear value. A scoping review using the Technology Acceptance Model (TAM) identified key factors affecting usability and usefulness. The second part applies behavioural science to understand why older adults often reject camera-based systems, highlighting psychological barriers such as low perceived need and fear of being watched. The concept of “future self-continuity” is introduced as a way to increase acceptance by helping users connect their current actions to future health benefits. Finally, the report reviews technical and behavioural strategies to reduce privacy concerns, such as using unobtrusive design or improving user control. The findings will guide future interventions and will be updated in a later report.
This deliverable provides a final update on research into camera-based technologies that support older adults living at home with multiple chronic conditions. It builds on earlier work (D3.4) and focuses on three main areas. First, it finalises research by ESR 8 on using behaviour change theory to improve the design and uptake of these technologies, particularly how to encourage older adults to accept and benefit from visual systems for managing multimorbidity. Second, it includes findings from ESR 17 (who replaced ESR 7) on healthcare professionals’ views about using camera systems in telehealth, based on a scoping review of real-life settings. Third, it brings together results from both ESRs 8 and 17 on how users perceive privacy, data protection, and ethical concerns, offering insights for future development and deployment of camera-based AAL tools. The report is currently under a one-year embargo to protect unpublished findings, and it will be publicly available from 1 March 2026.
This deliverable reviews key concepts, methods, and policy frameworks for achieving algorithmic fairness, especially in the context of artificial intelligence and automated decision-making. It outlines three types of bias—pre-existing, technical, and emergent—and explains how these can lead to various forms of discrimination, including direct, indirect, intersectional, and emergent. The report presents fairness metrics such as demographic parity, equal opportunity, and counterfactual fairness, and discusses their strengths and trade-offs. It describes strategies for bias detection and mitigation across the machine learning pipeline, including pre-processing (e.g. reweighing and data repair), in-processing (e.g. adversarial debiasing), and post-processing (e.g. outcome adjustment). Tools like Fairlearn, AI Fairness 360, and Aequitas are compared based on functionality, licensing, and usability. The report also addresses the importance of high-quality, representative data, offering practical recommendations on dataset documentation and transparency (e.g. datasheets, nutrition labels). Finally, it examines regulatory and ethical frameworks, including GDPR, the EU AI Act, ISO standards, and voluntary guidelines, to support fair and responsible AI development and deployment.
This document outlines the Dissemination and Communication Plan for VisuAAL, defining the implementation and evaluation measures that will be employed to effectively communicate about project objectives and activities, to disseminate project outputs, and to ensure the best exploitation of its results. It will further serve as a reference manual for the project, and supported partners in implementing the project dissemination and communication strategies as outlined in this document.
This deliverable should be referred to in conjunction with D4.2 Websites and profiles in social networks (M2); D4.3 Corporate image (M2); D4.4 Programme booklet and presentations (M3); D4.5 Leaflets presenting individual research projects (M9); D5.1 Feedback questionnaires (M9) and D6.1 Advertising of ESR positions (M3-M8).
This document outlines the functional requirements and the IT infrastructure required to develop the visuAAL website. This website has been designed using the visual guidelines (logo, images, colours) presented in Deliverable D4.3. Corporate image. Different menu options have been initially included. These options may vary during the project as some of them will be hidden/activated as needed.
The visuAAL project has registered the domain name visuaal-itn.eu as the main URL for the website.
Additionally, profiles for the visuAAL project have been created in several social networks to enhance visibility and to interact with the users:
This document presents the logotype and other corporate image of the visuAAL project in order to be used in the website, reports, presentation, brochures, and any other dissemination and communication outcome related to the project. This corporate image should create an easy identifiable image of the project among the research community and other stakeholders.