As smart cities continue to advance, the demand for secure, automated, and real-time healthcare services is growing to ensure sustainable and high-quality healthcare monitoring. This research introduces a cloud-based framework that integrates smart healthcare devices, environments, and stakeholders within smart cities to enhance the affordability, accessibility, and security of healthcare services. The primary objective is to develop a cloud-based system for real-time voice pathology detection by analyzing voice and electroglottographic (EGG) signals to accurately differentiate between normal and pathological conditions. By leveraging machine learning models such as Gaussian Mixture Models (GMM) for voice disorder classification, healthcare monitoring can be significantly improved, enabling early diagnosis and intervention. Furthermore, this framework aims to enhance the accessibility and scalability of healthcare services by ensuring secure, automated, and remote health monitoring in smart city environments. The proposed system collects voice and EGG signals from internet-connected devices, transmitting them to the cloud for advanced data analysis. A case study on voice pathology detection (VPD) demonstrated the effectiveness of this approach, where local features extracted from voice signals and shape and cepstral features from EGG signals were classified using a GMM, achieving an accuracy of over 93%. The results are then communicated to registered healthcare professionals for definitive diagnosis and appropriate action. By addressing the complex healthcare needs of smart city citizens, this framework provides a secure, scalable, and sustainable solution for real-time healthcare monitoring and decision-making, contributing to the advancement of smart and efficient healthcare services.
Journal of Theoretical and Applied Information Technology (Mon,) studied this question.