## 3 Results
### Search Results
#### 3.1.3 Elderly Support and AL  
The studies illustrated in Table 3 focus on various AI and HCI technologies designed for elderly support and AL, with several applications geared towards enhancing daily activities and mobility. One major area of focus is robotic assistance for elderly care, where robots are deployed for tasks such as household activities and personal assistance. To that end, [PERSON] et al. [51] explored the factors influencing the use of AI-enabled humanoid robots, gathering insights from caregivers and care receivers through questionnaires and focus groups. Their findings highlighted the importance of robots in performing household tasks and providing communication support. Similarly, [PERSON] et al. [52] examined smart home systems for individuals with dementia and their caregivers, utilizing tablet-based puzzles, chatbots, smartwatches, and ambient sensors. Their study emphasized how participatory design processes can foster more effective, inclusive, and rapid innovation in public health applications.  
Several studies have investigated specific application domains in AT for mobility and physical support, specifically related to the elderly. To that end, [PERSON] et al. [53] used the TIAGo robot, shown in Figure 2, combined with CNN, LSTM, and temporal convolutional network (TCN) models for multi-modal activity recognition, achieving an impressive 98.61% accuracy in action recognition under realistic scenarios, further enhancing assistive robots' effectiveness in daily living. Furthermore, [PERSON] et al. [54] introduced the ASTRO robot, which uses decision trees for walking assistance, resulting in 94.2% driving path accuracy and positive user experiences. These studies highlight the growing role of AI and robotics in improving the quality of life of elderly individuals by providing both physical support and enhancing interaction in everyday tasks.  
Finally, further studies investigated robotic manipulation and support systems designed to improve independence in ADLs. [PERSON] et al. [58] employed the PR2 robot with a seven-DoF arm and a simple ANN to assist in dressing and bathing tasks, achieving precision with minimal applied force. Meanwhile, [PERSON] et al. [57] focused on robotic manipulation for assisted drinking, employing the Kinova Jaco robotic arm with face and landmark detection to successfully deliver cups with a success rate of 99.54%, as depicted in Figure 2. This research demonstrates significant advances in robotic assistance for elderly individuals with mobility limitations. Focusing on a different domain, [PERSON] et al. [55] used the Dobot magician manipulator integrated with the YOLOv5 architecture for a visually impaired assistive system, achieving 99.3% mean average precision (mAP) in object detection and reducing the time for assisted grasping.  
Collectively, these studies demonstrate that elderly support systems perform remarkably in controlled settings. Nonetheless, scalability, robustness under real-world variability, and user acceptance remain critical areas for future research [59]. In particular, future work should more rigorously assess system usability over extended periods and under conditions of user fatigue or cognitive decline to ensure sustained effectiveness and adoption in real-world elderly care settings.