## 1 Introduction
The study in [20] proposed an Internet of Medical Things (IoMT)-based framework for home healthcare of the elderly, combining wireless body area networks (WBANs) for continuous monitoring, deep learning, and federated learning for data processing with 5G for rapid data transmission. The framework also utilizes cloud, edge, and fog computing to support the federated learning process. However, potential challenges include ensuring seamless integration of these technologies and managing the complexity of real-time data processing and transmission in a multi-layered architecture. The authors in [21] proposed the FEEL smart home healthcare system for elderly care, utilizing federated learning, a knowledge graph, and deep learning for health monitoring and recommendations. While effective in certain areas, the study did not fully address the integration challenges of AI models and real-time data accuracy across varied environments. A spectral clustering method and MFO-Edge caching algorithm to optimize e-health IoT networks by clustering patients and connecting them to SDN edge nodes for efficient data caching was presented in [22]. While the approach improved performance in terms of data retrieval delays and cache hit rates, it did not fully address the need for real-time adaptation to dynamic network conditions and personalized caching strategies. The work in [23] proposed an SDN-based model to enable global connectivity between IoT devices and medical services, particularly for integrating Nano networks within the human body with healthcare systems. However, the study highlighted the challenge of effectively managing the integration and real-time data sharing across diverse and globally distributed IoT networks, which remains a significant hurdle in achieving seamless healthcare connectivity. Similarly, a distributed edge computing-based IoT framework combining cloud computing, edge computing, and machine learning to optimize real-time data processing in medical sensor networks was proposed in [24]. However, the study did not fully address the challenge of efficiently handling the overwhelming amount of data collected by medical sensors, which is crucial for providing timely and relevant healthcare insights. Therefore, this paper proposes an Edge Computational Intelligence (ECI)-based real-time elderly ECL-TeleCaring system to address challenges such as high latency, inconsistent service quality, inadequate personalization, and the need for stability and transparency in telecaring systems. The proposed system is specifically designed to ensure both QoS and QoE in elder care services by addressing these key challenges. To reduce latency, the system integrates Software-Defined Networking (SDN) and Mobile Edge Computing (MEC) technologies within the network domain to reduce delays and enable real-time bidirectional communication between the master and slave domains, which is critical for timely interventions in elder care. To ensure consistent service quality, a dual Long Short-Term Memory (LSTM) model is implemented at the edge for real-time remote activity prediction and critical decision making, thereby maintaining service quality even under varying conditions. The system also addresses the challenge of inadequate personalization by processing and analyzing large volumes of data at the edge, allowing it to continuously learn from and adapt to individual user needs, thereby providing a personalized care experience tailored to the specific requirements of each elderly individual. Furthermore, the proposed system is designed to enhance stability and transparency, particularly in telecaring scenarios, through edge intelligence to dynamically adapt to changing conditions and communication delays, ensuring that control signals between the master and slave domains remain stable and transparent, effectively overcoming the major shortcomings in existing telecaring systems.