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Interactive installation art provides a distinctive context for examining collective emotion, yet most prior studies have relied on laboratory or longitudinal data that are impractical in public cultural settings. This study applied Hidden Markov Models (HMMs) to self-reported well-being data from HappyHere , a participatory light installation at the National Galleries of Scotland. Despite the cross-sectional design, the HMM framework enabled inference of latent affective states and probabilistic differentiation patterns, stability, and convergence patterns. The results show four key findings. First, three qualitatively distinct latent states were identified: a low/negative cluster (M ≈ 1.5), a moderately positive cluster (M ≈ 3.5), and a ceiling-level, highly positive cluster (all M = 5.0), confirming clear differentiation (H1). Second, positive states proved the most stable, with the highest self-transition probability (0.875) and the longest dwell time (≈ 3.4 steps), supporting H2. Third, neutral states were comparatively unstable, showing the lowest self-transition probability (0.093) and a tendency to shift toward positivity, consistent with H3. Finally, the stationary distribution strongly favored positivity, with positive states Reaching 86.3%, neutral states 7.5%, and negative states 6.2%. Minor diurnal variation was observed, but effect sizes were negligible, confirming the enduring predominance of positivity (H4). The findings demonstrate that even cross-sectional cultural datasets can yield meaningful insights through probabilistic modelling. Positive affect emerged as the most stable and dominant attractor, underscoring the capacity of participatory art to regulate emotion and foster collective well-being.

This study presents a comprehensive literature review on the convergence of affective computing, interactive installation art, multi-dimensional sensory stimulation, and artificial intelligence (AI) in measuring emotional responses, demonstrating the potential of artificial intelligence in emotion recognition as a tool for sustainable development. It addresses the problem of understanding emotional response and measurement in the context of interactive installation art under artificial intelligence (AI), emphasizing sustainability as a key factor. The study aims to fill the existing research gaps by examining three key aspects: sensory stimulation, multi-dimensional interactions, and engagement, which have been identified as significant contributors to profound emotional responses in interactive installation art. The proposed approach involves conducting a process analysis of emotional responses to interactive installation art, aiming to develop a conceptual framework that explores the variables influencing emotional responses. This study formulates hypotheses that make specific predictions about the relationships between sensory stimulation, multi-dimensional interactions, engagement, and emotional responses. By employing the ASSURE model combined with experimental design, the research methodology ensures a systematic and comprehensive study implementation. The implications of this project lie in advancing the understanding of emotional experiences in interactive installation art under AI, providing insights into the underlying mechanisms that drive these experiences, and their influence on individual well-being from a sustainable perspective. The contributions of this research include bridging the identified research gaps, refining theoretical frameworks, and guiding the design of more impactful and emotionally resonant interactive artworks with sustainability in mind. This research seeks not only to fill the existing gaps in understanding emotional experiences in interactive installation art, but also to guide the development of immersive and emotionally engaging installations, ultimately advancing the broader field of human–computer interaction, promoting individual well-being, and contribute to sustainable development.

This paper summarizes the social function of public art under the perspective of digital technology, takes interactive installation art as the main research path, and proposes the design scheme of interactive installation art in community public space with regard to three aspects: sense of context, sense of user participation, and practicality of the installation. Through the way of practical research, analyze the general overview of the subject area and design an installation art scheme that meets the subject area. Set up an example investigation to research and analyze how public art installations affect the interaction of community users. In response to the two installation art interaction methods, users found the operation of the installation MI to be more difficult than that of the installation MT, with the degree of difficulty for both being 3.43 and 4.016, respectively. As for the level of interest, users are more interested in the device MT with a level value of 3.781. The range of [4.3,4.5] is where the mean values of most measured indicators are concentrated in the satisfaction analysis. Meanwhile, the measured values of all the indicators are greater than 4. The overall design of the interactive installation art in the public space is good, and the community is accepting of it.

FeelOpo is an interactive art installation that allows contact with fragments of the immaterial heritage of the Oporto City in the North of Portugal. Through location-based storytelling of the living city, this interactive installation allows visitors to explore, at different levels, several typical characteristics of this city, addressing aspects of cultural identity based on contrasting images and videos. The visitors feel and explore visual stories of the live city, through a process of appropriation and articulation of these narratives, generating an expansion of this intangible heritage.

Currently, display design is no longer a pure exhibition of items with the development of science and technology as well as the society, which integrates the power of art and audience's participation and interaction. Based on this background, interactive multimedia art installation has been applied to display design. The article first introduces the background of the application of interactive multimedia art installation in display design, then summarizes the definition, development and advantages of interactive multimedia art, and lastly analyses how interactive multimedia installation art can be applied in display design.

The integration of Internet of Things (IoT) technologies with deep learning has introduced powerful opportunities for advancing cross-media art and design. This paper proposed DeepFusionNet, an IoT-driven multimodal classification framework developed to process real-time visual, auditory, and motion data acquired from distributed sensor networks. Rather than generating new content, the system classifies contextual input states to activate predefined artistic modules within interactive multimedia environments. The architecture of DeepFusionNet integrates Convolutional Neural Networks (CNNs) for spatial feature extraction, as well as Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) layers for modeling temporal dependencies in auditory and motion data. Additionally, it features fully connected layers for multimodal feature fusion and final classification. Input data undergoes comprehensive preprocessing, including normalization, imputation, noise filtering, and augmentation, to ensure consistent and high-quality multimodal representations. Extracted features from each modality are fused within the network to identify user interaction contexts that guide adaptive system responses. Unlike existing multimodal transformer-based frameworks, DeepFusionNet prioritizes low-latency and synchronized IoT processing, offering a Lightweight yet robust alternative for real-time interaction. Employing deep multimodal fusion rather than simple rule-based triggers ensures contextual awareness, scalability, and resilience in interactive art installations. Experimental evaluations demonstrate that DeepFusionNet achieves high performance, with 94.2% accuracy, 92.5% sensitivity, 96.1% specificity, 93.8% F1-score, 95.0% precision, MCC of 0.846, and an AUC of 0.96. Furthermore, the model achieves a 15% reduction in latency compared to baseline frameworks. The DeepFusionNet offers a scalable and real-time infrastructure for user-aware, IoT-enhanced cross-media art applications.

This paper presents an attention-enhanced deep reinforcement learning control system for thermal-visual collaborative optimization in multi-sensory art installations. The proposed system integrates three key innovations: (1) an attention-based DDPG algorithm with dynamic modality weighting (α = 0.6–0.8 for thermal, 0.6–0.7 for visual features), (2) adaptive sensor fusion combining Kalman and particle filtering with 8 ± 2 ms processing latency, and (3) hierarchical four-layer architecture achieving 65% control accuracy improvement and 40% response time reduction compared to traditional approaches. The system architecture incorporates a hierarchical four-layer design with perception, fusion, decision-making, and execution components. The core innovation lies in an attention-based deep reinforcement learning algorithm that dynamically processes multi-modal sensory inputs and optimizes thermal-visual coordination through continuous learning. The algorithm employs spatiotemporal alignment mechanisms, adaptive feature weighting, and collaborative optimization strategies to achieve superior control performance. Experimental validation demonstrates quantified improvements over conventional methods: control accuracy improved from 0.247 ± 0.067 RMSE (PID baseline) to 0.085 ± 0.012 RMSE (proposed method), representing 65% improvement; response times reduced from 78 ± 22 ms (fuzzy control) to 45 ± 8 ms (proposed method), achieving 40% improvement; energy efficiency increased from baseline consumption to 23% reduction through intelligent coordination. Real-world deployment results confirm practical effectiveness with measured user satisfaction scores of 4.1 ± 0.7 on a 5-point scale and system availability of 98.5% over 186-hour continuous operation periods. The proposed system enables more sophisticated multi-sensory experiences while maintaining artistic integrity and provides a foundation for advanced digital art technologies.

iThis book is about the digital interface and its use in interactive new media art installations. It examines the aesthetic aspects of the interface through a theoretical exploration of new media artists, who create, and tactically deploy, digital interfaces in their work in order to question the socio-cultural stakes of a technology that shapes and reshapes relationships between humans and non-humans. In this way, it shows how use of the digital interface provides us with a critical framework for understanding our relationship with technology.

Project MindCatcher is an art/research project which has the intention to explore the potential of interactive artistic environments for measuring and a better understanding of creative experience and contribute to further development of co-creative artistic/design process. For that purpose, we created the Floor Interface and collected a set of data from its interactive points such as: personal information from the login process, abstract multi-sensory artefacts created by visitors, visitors’ walking paths from the floor interface, video shooting of their behaviour on the floor interface during the sessions, results from the interviews which were done with visitors and personal observations.

Interactive multimedia art installations in public spaces are evolving with the integration of real-time (RT) digital control and visualization technologies, enabling dynamic and immersive user experiences. These installations transform urban environments into responsive, participatory spaces through audiovisual interactivity. However, existing methods often face limitations such as inflexible architectures, latency in data processing, and challenges in real-time synchronizing multimodal inputs and outputs. These issues hinder scalability and reduce the creative possibilities of interactive systems in public settings. This study proposes a Node-based Real-Time Multimedia Architecture using Max/MSP and (TD) Touch Designer (MAX/MSP-TD) to address these challenges. The framework leverages the strengths of visual programming environments, allowing seamless integration of real-time sensor data, audio analysis, and generative visuals through modular and reusable components. The proposed method is applied in an urban installation where sound and movement data from the public are captured and translated into interactive light and sound projections in real time. This encourages public engagement and offers a platform for creative expression within the built environment.