Maximizing Efficiency in Telemedicine: An IoT-Based Artificial Intelligence Optimization Framework for Health Analysis

  • Saad Rasool
  • Aftab Tariq
  • Yawar Hayat
Keywords: Internet of things, artificial intelligence, telemedicine, health monitoring, data analysis


This paper delves into a comprehensive exploration of health IoT architecture and its implementation technologies, considering both theoretical and practical aspects. The study emphasizes the theoretical importance and practical applicability of the research. Key areas covered include cloud fusion health IoT architecture, multimodal information acquisition within the health IoT perception layer, multi-level service quality assurance based on human LAN in health IoT, and emotional perception and interaction within the health IoT context. In terms of health IoT architecture, the proposal introduces a cloud-converged approach that deeply integrates the health cloud platform and perception layer. This integration involves utilizing various communication technologies to enhance user experience, fostering closer connections between health IoT applications and individuals. The paper details basic concepts and main components of multimodal sensing information collection, outlining the design and implementation of a health monitoring cloud robotics platform. This platform involves robotics-based multimodal data sensing and aggregation, as well as the collection of comfortable and sustainable physiological signals through smart clothing. The feasibility and performance of the Quality of Service (QoS) framework proposed in this paper are validated through computer simulations. Migration learning is employed for emotion data labeling, and continuous conditional random fields are utilized for emotion identification based on data from smartphones and smart clothing. The paper concludes with decision layer fusion for emotion classification prediction.


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How to Cite
Rasool, S., Tariq, A., & Hayat, Y. (2023). Maximizing Efficiency in Telemedicine: An IoT-Based Artificial Intelligence Optimization Framework for Health Analysis. European Journal of Science, Innovation and Technology, 3(6), 48-61. Retrieved from