Smart urban planning: Intelligent cognitive analysis of healthcare data in cloud-based IoT

Abstract

In recent years, there has been a growing recognition among competent city developers and researchers regarding the critical importance of integrating healthcare data analysis into smart urban planning. This research study focuses on enhancing the healthcare system using a cognitive-based cloud framework. We employ the Internet of Things (IoT) to collect patient data and utilize a cloud-based cognitive framework for real-time patient monitoring. We aim to achieve cost-effective and high-quality healthcare. Deep learning techniques are applied to assess the health status and obtain experimental results. Specifically, we conduct pathology detection and classification using Electroencephalography (EEG). Multimodal patient health data, including EEG signals, are recorded using an EEG sensor. Intelligent IoT devices transmit the EEG signals from patients to the cloud, where they undergo processing and are forwarded to a cognitive module. The system tracks various sensor readings, such as facial expressions, speech, EEG, movements, and gestures, to determine the patient's condition. To classify the EEG signals as pathological or normal, our proposed method employs a Transfer Learning-based Convolutional Neural Network with Long Short-Term Memory (CNN-LSTM) and the Kruskal-Wallis (KW) method. We demonstrate the effectiveness of our approach, outperforming existing methods on the same dataset, achieving an impressive accuracy of 95.13% in identifying EEG pathologies.