Early Recognition of Infant Discomfort Through Real Time Analysis of cries- A Digital Twin Based Approach

Abstract

The ability to accurately interpret an infant's cries is crucial for providing appropriate and timely care. However, many caregivers find it challenging to understand the needs of a crying infant, leading to misinterpretations and delayed interventions. This project aims to address this issue by developing a system based on digital twin technology to enhance infant care through accurate cry detection and classification. The concept of the digital twin is the foundation of this project, where a virtual model replicates the physical entity, in this case, the infant. By integrating Internet of Things (IoT) technologies, we establish real-time connectivity between the physical baby and its digital representation. This digital twin captures and processes data from sensors attached to the infant, providing a comprehensive and dynamic model of the baby's condition. The digital twin serves as the core of the system, facilitating continuous monitoring and data collection. We employ advanced machine learning (ML) and deep learning (DL) techniques to analyze the collected data. Two primary models were developed within the system : one for cry detection and another for classifying the reasons for crying. These models are trained using a variety of neural network architectures, including Multi-Layer Perceptrons (MLP) and Long Short-Term Memory (LSTM) networks combined with Convolutional Neural Networks (CNN). Additionally, genetic algorithms are used to optimize the hyperparameters of these models, ensuring high accuracy and performance. The results demonstrate the system's effectiveness through simulations and real-world tests, significantly improving the accuracy of cry interpretation and, consequently, infant care. The MLP model achieves an impressive accuracy rate of 96.67%, while the LSTM-CNN model attains a perfect accuracy rate of 100%.