Predictive Parenting: An IoT-Enabled Cradle System with AI-Driven Sleep Pattern Analysis

This research study introduces an IoT-Enabled Cradle System with the "DreamFlowRNN" algorithm, combining Recurrent Neural Networks (RNN) and Flow-based models. Equipped with smart sensors, the cradle monitors infants' physiological and environmental data during sleep. DreamFlowRNN ana...

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Bibliographic Details
Published in2024 5th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI) pp. 264 - 271
Main Authors Sakthidevi, I., Shankar, S. Vijaya, Santhana Krishnan, R., Kuruvilla, Sincy Elezebeth, Bharath, M., Gopikumar, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 18.01.2024
Subjects
Artificial Intelligence
Classification algorithms
Cradle System
Internet of Things
Pattern analysis
Pediatrics
Prediction algorithms
Predictive Parenting
Real-time systems
Recurrent neural networks
Sleep Pattern Analysis
Smart sensors
Support vector machines
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DOI10.1109/ICMCSI61536.2024.00045

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Summary:This research study introduces an IoT-Enabled Cradle System with the "DreamFlowRNN" algorithm, combining Recurrent Neural Networks (RNN) and Flow-based models. Equipped with smart sensors, the cradle monitors infants' physiological and environmental data during sleep. DreamFlowRNN analyzes this information, classify sleep stages, detect disturbances, and predict future patterns. The proposed system aims to provide predictive parenting capabilities by offering valuable insights into infants' sleep behavior. Extensive simulations, comparing DreamFlowRNN to existing algorithms like LSTM, CNN, and SVM, reveal its superior performance in accurate sleep stage classification, disturbance detection, and generation of realistic synthetic sleep patterns. The proposed system stands out with higher F1-score, precision, inception score, and lower Frechet Inception Distance (FID) value, emphasizing its efficacy in enhancing the well-being of infants through advanced AI-driven sleep pattern analysis.
DOI:10.1109/ICMCSI61536.2024.00045