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Gabriel Vigliensoni

Publications and source records attributed to Gabriel Vigliensoni.

7 recordsLinked to original sources

Beyond Score-Based Gamification: Designing Spatiotemporal and Musical Experiences for VR Neck Rehabilitation

Pain-related anxiety and fear of movement are major barriers to adherence and therapeutic outcomes in rehabilitation exercises for chronic neck pain. Virtual reality enables the design of immersive experiences that can transform repetitive therapeutic movements into engaging and emotionally supportive interactions. In this exploratory work, we investigate how experience-oriented gamification can reduce anxiety and improve user experience during VR-based neck range-of-motion exercises. We introduce two novel interaction paradigms that embed therapeutic neck movements within multisensory VR experiences. The first paradigm, Spatiotemporal Progression, couples head-tracked trajectories with environmental progression in a tropical island setting, where movement segments dynamically transform time of day, weather, and spatial location as experiential rewards. The second paradigm, Musical Interaction, maps movement segments to meditative music notes layered with relaxing ambient soundscapes. We evaluate these designs against a conventional score-based gamification baseline in a controlled user study with 20 non-patient participants. We assess usability and user experience through subjective measures, exercise performance with motion tracking, and anxiety modulation using the Subjective Units of Distress Scale, heart rate, and skin conductance. Our findings suggest that, in comparison with traditional score-based gamification design, immersive environmental and musical feedback show better potential to reduce anxiety and improve user experience, with little to no impact on successful performance of the exercise. Our preliminary results highlight the potential value of experience-based interaction design for VR rehabilitation, suggesting an alternative to performance-centric gamification that prioritizes emotional engagement without compromising therapeutic efficacy.

cs.HC

Proceedings of The Fourth International Workshop on eXplainable AI for the Arts (XAIxArts 4)

The fourth workshop on Explainable AI for the Arts (XAIxArts) continues to bring together and expand a community of researchers and creative practitioners in Human-Computer Interaction (HCI), Interaction Design, AI, eXplainable AI (XAI), and Digital Arts to explore the role of XAI for the Arts. XAI is a key concern of Responsible and Human-Centred AI, emphasising HCI techniques that make opaque AI models more understandable to people. XAIxArts offers a distinctive lens to examine explainability through creative and artistic domains. The previous workshops explored the landscape and the speculative futures of AI in creative processes. To respond to emerging challenges and contribute to creative and societal transformation more broadly, this workshop focuses on the operationalisation of XAI in the Arts. Specifically, we will: i) critically reflect on emerging practices that encourage diversity and inclusivity in XAI; ii) collectively ideate a library of missing projects to encourage future collaborations and speculations; iii) scope the development of a resource hub for open XAIxArts projects to archive tangible XAI interventions and facilitate future community building with the wider discourse on Human-Centred AI.

cs.HC

fCrit: A Visual Explanation System for Furniture Design Creative Support

We introduce fCrit, a dialogue-based AI system designed to critique furniture design with a focus on explainability. Grounded in reflective learning and formal analysis, fCrit employs a multi-agent architecture informed by a structured design knowledge base. We argue that explainability in the arts should not only make AI reasoning transparent but also adapt to the ways users think and talk about their designs. We demonstrate how fCrit supports this process by tailoring explanations to users' design language and cognitive framing. This work contributes to Human-Centered Explainable AI (HCXAI) in creative practice, advancing domain-specific methods for situated, dialogic, and visually grounded AI support.

cs.HC

XAIxArts Manifesto: Explainable AI for the Arts

Explainable AI (XAI) is concerned with how to make AI models more understandable to people. To date these explanations have predominantly been technocentric - mechanistic or productivity oriented. This paper introduces the Explainable AI for the Arts (XAIxArts) manifesto to provoke new ways of thinking about explainability and AI beyond technocentric discourses. Manifestos offer a means to communicate ideas, amplify unheard voices, and foster reflection on practice. To supports the co-creation and revision of the XAIxArts manifesto we combine a World Café style discussion format with a living manifesto to question four core themes: 1) Empowerment, Inclusion, and Fairness; 2) Valuing Artistic Practice; 3) Hacking and Glitches; and 4) Openness. Through our interactive living manifesto experience we invite participants to actively engage in shaping this XIAxArts vision within the CHI community and beyond.

cs.HC

Explainability Paths for Sustained Artistic Practice with AI

The development of AI-driven generative audio mirrors broader AI trends, often prioritizing immediate accessibility at the expense of explainability. Consequently, integrating such tools into sustained artistic practice remains a significant challenge. In this paper, we explore several paths to improve explainability, drawing primarily from our research-creation practice in training and implementing generative audio models. As practical provisions for improved explainability, we highlight human agency over training materials, the viability of small-scale datasets, the facilitation of the iterative creative process, and the integration of interactive machine learning as a mapping tool. Importantly, these steps aim to enhance human agency over generative AI systems not only during model inference, but also when curating and preprocessing training data as well as during the training phase of models.

cs.SD