Upgraded Interactive Experience for Movie Audiences Driven by IoT Technology

An interactive experience for audiences provides an innovative approach to traditional cinema viewing, allowing them to participate in the story in real time. This dynamic format extends above passive watching by including aspects that allow viewers to affect plot events, character relationships, and the overall movie experience. The Internet of Things (IoT) enables movie theaters to provide individualized experiences by integrating a range of gadgets, including wearable technology, smartphones, and in-theater sensors, to improve audience engagement. The purpose of the research is to explore the upgraded interactive experience for movie audiences driven by IoT technology in the context of artificial intelligence. The existing systems have difficulty with real-time, correct interpretation of user feedback because of technological constraints, which results in inadequate viewing environment modifications. To enhance the interactive experience for movie audiences, the Osprey Optimized Efficient Recurrent Neural Network (OO-ERNN) model predicts audience engagement and improves their experience by integrating IoT-based solutions. OO-ERNN is the basis for a revolutionary movie recommendation model that takes user interests and related relational and semantic information between films into consideration. To provide a more engaging and dynamic movie experience, this model incorporates the data that was preprocessed using data cleaning and normalization. Then applied to standardize the dataset, improving model performance, feature extraction using Fast Fourier Transform (FFT) to analyze real-time data from IoT devices, the finding reveals that the proposed model achieves superior performance using Python 3.11, with accuracy (97.71%), precision (95.13%), [Formula: see text]1 score (94.32%), MSE (0.0312), MAE (0.0513), MAPE (0.0312), and [Formula: see text] (0.9953), with lower loss function values and improved audience engagements. Deep learning and IoT integration highlight the potential to completely transform the movie-watching experience by offering more varied and deeper interactions and providing possibilities for future advancements in audience-centric entertainment systems.

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Publication Details

Journal
International Journal of High Speed Electronics and Systems
Published
2025-07-05
DOI
https://doi.org/10.1142/s0129156425406667
Primary Topic
Image and Video Quality Assessment
Type
article
Field-Weighted Citation Impact
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article

Upgraded Interactive Experience for Movie Audiences Driven by IoT Technology

Cheng Dong, Long Wang
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International Journal of High Speed Electronics and Systems
Image and Video Quality Assessment
article

Upgraded Interactive Experience for Movie Audiences Driven by IoT Technology

Cheng Dong, Long Wang
article en

Abstract

An interactive experience for audiences provides an innovative approach to traditional cinema viewing, allowing them to participate in the story in real time. This dynamic format extends above passive watching by including aspects that allow viewers to affect plot events, character relationships, and the overall movie experience. The Internet of Things (IoT) enables movie theaters to provide individualized experiences by integrating a range of gadgets, including wearable technology, smartphones, and in-theater sensors, to improve audience engagement. The purpose of the research is to explore the upgraded interactive experience for movie audiences driven by IoT technology in the context of artificial intelligence. The existing systems have difficulty with real-time, correct interpretation of user feedback because of technological constraints, which results in inadequate viewing environment modifications. To enhance the interactive experience for movie audiences, the Osprey Optimized Efficient Recurrent Neural Network (OO-ERNN) model predicts audience engagement and improves their experience by integrating IoT-based solutions. OO-ERNN is the basis for a revolutionary movie recommendation model that takes user interests and related relational and semantic information between films into consideration. To provide a more engaging and dynamic movie experience, this model incorporates the data that was preprocessed using data cleaning and normalization. Then applied to standardize the dataset, improving model performance, feature extraction using Fast Fourier Transform (FFT) to analyze real-time data from IoT devices, the finding reveals that the proposed model achieves superior performance using Python 3.11, with accuracy (97.71%), precision (95.13%), [Formula: see text]1 score (94.32%), MSE (0.0312), MAE (0.0513), MAPE (0.0312), and [Formula: see text] (0.9953), with lower loss function values and improved audience engagements. Deep learning and IoT integration highlight the potential to completely transform the movie-watching experience by offering more varied and deeper interactions and providing possibilities for future advancements in audience-centric entertainment systems.

International Journal of High Speed Electronics and Systems
Changsha University of Science and Technology (CN)
Openalex Percentile: Top 86%
Image and Video Quality Assessment
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