IoT-enabled intelligent art design framework for adaptive digital media creation using deep learning and human machine interaction

With the advent of fast-growing Internet of Things (IoT) technologies, intelligent creative systems offer new opportunities for adaptive digital media output. Current computational art design frameworks, however, do not sufficiently incorporate IoT-driven human-machine interaction mechanisms with deep-learning-based aesthetic and style intelligence. Accordingly, this paper introduces IoT ArtNet, a novel IoT-based intelligent multimodal framework for adaptive digital media creation. To achieve three different deep learning tasks, IoT ArtNet uses the Art Style Representation Module trained on the WikiArt Art Movements and Styles dataset for artistic style classification, the Aesthetic Intelligence Module trained on the AVA (Aesthetic Visual Analysis) dataset for aesthetic quality prediction, and the Gesture-Based Human-Machine Interaction Module trained on the LeapGestRecog dataset for gesture command recognition using sensors. The framework achieves multimodal fusion of style embeddings, aesthetic quality scores, and gesture control via attention-weighted fusion, enabling adaptive media output decisions in a new Adaptive Media Fusion Module. The framework includes edge and cloud inference layers to support low-latency, IoT-oriented deployment. Offline prototype benchmark evaluations indicate that IoT ArtNet achieves 93.2% style classification accuracy, 96.1% gesture recognition accuracy, and an aesthetic prediction MAE of 0.29 on their respective datasets. Ablation studies examine each module’s contribution to overall system performance. The proposed framework provides a practical, IoT-oriented design workflow for adaptive digital media creation, applicable to smart environments, interactive exhibitions, and the creative industries.