An Innovative Approach for Revolutionizing Pediatric Health Monitoring in Real-Time Activity Recognition Utilizing CNN-LSTM-ELM
Keywords:
Paediatric Activity Recognition, Convolutional Neural Networks, Long Short-Term Memory Networks, Extreme Learning Machine, Real-Time Recognition, Healthcare Monitoring.Abstract
Pediatric activity recognition is an essential part of many healthcare and childcare applications, allowing for the monitoring and evaluation of children's physical development. In this study, a novel real-time pediatric activity recognition system is proposed, which combines the advantages of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for extraction of features, followed by an Extreme Learning Machine (ELM) classifier for accurate activity categorization. It initially generated an extensive dataset made up of footage of kid-friendly activities that had been carefully labelled with activity categories. A two-step procedure is employed, starting with the use of a CNN model to extract discriminative spatial features from video frames that has been pre-trained on a large dataset. The image signals available in pediatric activities are richly represented by these elements. In order to capture temporal relationships within the series of feature vectors, it incorporates an LSTM network after feature extraction. Further improving the recognition accuracy, this LSTM-based sequence modelling is skilled at identifying subtle activity patterns and transitions over time. The key component of this development is the addition of an ELM classifier after the LSTM layer. ELM, which is renowned for its ability to train quickly and effectively, utilizes the temporal context stored by the LSTM to conduct real-time activity classification with astounding speed and accuracy. As a result, pediatric actions are recognized effectively and robustly. The CNN-LSTM-ELM model is utilized to analyze receiving images in order to do real-time recognition. The system is equipped with this framework to enable real-time decision-making in scenarios including healthcare and child care. The findings show that the suggested CNN-LSTM-ELM architecture demonstrates outstanding accuracy of 90.5% and efficiency in identifying a wide spectrum of pediatric activities, hence enhancing the capabilities of child-focused healthcare and wellbeing applications
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