Algorithms for digital cultural tourism ecological model with biomechanical considerations in VR scene interactions - Peng, Jing (Molecular & cellular biomechanics, 2025)
Digital cultural tourism is an emerging form of tourism that presents cultural heritage and tourism resources digitally to users, providing immersive travel experiences. However, traditional methods of constructing virtual scenes often rely on manual modeling, leading to low efficiency and high costs. The existing digital cultural tourism platforms mostly provide static and pre-set content, lacking interaction with users, making it difficult to achieve personalized recommendations and interactive experiences. In response to these issues, this article is based on VR (Virtual Reality) scene intelligent generation and interactive algorithms, and aims to optimize the overall synergy between the presentation of cultural resources and user experience by constructing a digital cultural tourism ecological model. Drawing on biomechanical principles, the study emphasizes the importance of natural user interactions and physical engagement in enhancing the immersive experience. Firstly, the Lindenmayer system (L-system) and parameterized generation rules are used to generate complex natural landscapes and architectural structures. Natural and textured scene details are added using the Perlin noise algorithm. Using GANs (Generative Adversarial Networks) technology, generative and discriminative networks are trained to generate more realistic VR scenes, further enhancing the realism and detail representation of the scenes. At the same time, a gesture recognition technology combining CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory) models, along with a speech recognition algorithm based on DNN (Deep Neural Networks), is adopted to enhance the natural interaction between users and virtual scenes. By combining collaborative filtering algorithms with user behavior data, personalized content recommendations are realized, enhancing user engagement and satisfaction. The efficiency test of scene modeling, the total time required to generate scenes using the model in this article is only 84 hours, which is much lower than manual modeling. In the interactive test, the highest success rate of the model in this article in gesture recognition reaches 94%. The experimental results have verified the advantages of the model in this article in improving scene modeling efficiency and enhancing immersive experiences through biomechanically informed interactions.