DESIGNING EMOTION-SENSITIVE INTERACTIVE ART USING BIOMETRIC SENSORS AND ADAPTIVE COMPUTING MODELS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i4s.2026.7491Keywords:
Emotion Recognition, Interactive Art, Biometric Sensors, Adaptive Computing, Deep Learning, Reinforcement LearningAbstract [English]
The dynamic terminal to technology and human perception, creativity and human expression, an emotion-sensitive interactive art is where creative art is capable of responding to the emotional state of the involved participants. The paper will give an elaborate layout of the evolution of adaptive art systems through the multimodal biometric sensor and intelligent computing model. Physiological stimuli to acquire real-time emotional stimuli are electroencephalography (EEG), electrocardiography (ECG), galvanic skin response (GSR) and eye-tracking measurements. Noise filtering, normalization and feature extraction are some of the advanced signal preprocessing algorithms that ensure high data representation. The machine learning and deep learning models that have been applied to classify emotional states with high precision are referred to as Emotional state SVM, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer-based models. The suggested system includes a dynamic computing system that is guided by the reinforcement learning, creating the possibility of active dialogue between the user emotions and artistic products. The system responses are constantly upgraded by the feedback control mechanisms, making them more engaging and individual. The proposed approach can be proved effective as experimental assessments show an increased rate of emotion recognition and responsiveness.
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Copyright (c) 2026 Dr. Ria Kohli , Suhas Bhise , Yan Zhao , Senthil Kumar A , Muhammad Muhammad Suleiman , Dr. Aneesh Wunnava
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