## 1 Introduction  
The rapid advancements in robotics and Artificial Intelligence (AI) have significantly transformed various sectors, including healthcare [1, 2]. One of the critical areas where these technologies have shown immense potential is in elder care services [3, 4]. The integration of robotics into elder care aims to address the increasing demand for high-quality care, driven by the aging global population. According to the World Health Organization (WHO), the number of people aged 60 years and older is expected to double by 2050, necessitating innovative solutions to meet their care needs [5]. Robotic systems in elder care can perform a range of tasks, from physical assistance and medication management to companionship and health monitoring [6, 7]. Robotic-assisted care systems have been extensively studied and developed to support individuals with various needs, ranging from physical assistance to social companionship. Early research highlighted the potential of robots like assistive mobile robots and robotic exoskeletons to enhance mobility and independence for users [8, 9]. For instance, the authors in [10] proposed Lio, a mobile robot platform with a multi-functional arm designed for human-robot interaction and personal care tasks, which has been successfully deployed in healthcare facilities. Similarly, the work in [11] demonstrated that 'Zora', a caring robot, positively impacted client interaction and activity levels in elderly care institutions. The authors in [12] conducted a study deploying the robot platform Pepper in a care home, revealing positive engagement from residents during robot-moderated training sessions. Despite these advancements, providing real-time elder care services presents several challenges to meet Quality of Service (QoS) and Quality of Experience (QoE) requirements [13]. The problem is particularly critical in environments where care must be provided remotely and the system must adapt to various dynamic conditions and individual needs. Traditional methods of remote care are often limited by latency, inconsistent service quality, and inadequate personalization [14]. According to the Third-Generation Partnership Project (3 GPP) Release 15 report, the Ultrareliable and Low-Latency Communication (URLLC) service offers a 99.9% reliability for a data packet of size 32 bytes and focuses on 1 ms communication latency [15]. However, activity sensor data along with haptic data in emerging applications like telecaring demand a higher packet rate with reliability greater than 99.9% under 1 ms latency. Moreover, several studies focused on integrating AI to enhance QoS and QoE in remote care settings. In [16], a telerobotic system for remote dementia care is proposed which combines a dual-arm collaborative robot with a wearable motion capture device, enabling caregivers to assist dementia patients remotely. A study in [17] conducted a randomized controlled trial to evaluate the impact of culturally competent AI in socially assistive robots, which improved emotional well-being significantly compared to usual care. The challenges of introducing care robots in Swedish elder care and identifying barriers such as technological beliefs, attitudes, and the need for knowledge and skills at various levels were explored in [18]. A comprehensive overview of social robots in therapy and healthcare is presented in [19], highlighting their potential benefits and the necessity for further research on their acceptability and perception. However, the aforementioned studies ignored the aspect of stability and transparency in elder caring teleoperation systems. In this regard, a system is essential to balance stability and transparency while meeting QoS and QoE requirements under varying communication delays and high packet rates. In addition to these developments, recent research has explored the combination of these technologies to create more robust telemedicine and elder care systems.