## 3 Results
### Search Results
#### 3.1.4 Emotional Expression and Virtual Companionship  
The studies in Table 4 explore the applications of AI and HCI in emotional expression and virtual companionship. To begin with, [PERSON] [60] explored assistive social robotics for engagement and therapy, emphasizing the importance of AI-driven social behaviors, interaction modalities, and design principles. This study, based on focus groups, interviews, and observations, provides valuable insights into how robotics can be effectively integrated \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Authors** & **Application Domain** & **Technology** & **Input Data** & **Validation** & **Employed** & **Results** \\ \hline [PERSON] et al., 2025 [51] & Factors of robotic assistants for elderly & AI-enabled humanoid robots & Focus groups and questionnaires & Real test on older adults & 82 caregivers and care & Robots performing household \\ [PERSON] et al., 2024 [55] & Visually impaired assistive system for & Dobot magician manipulator and grasping & RGB-D images, speech data, and vibration data & Real test on healthy subjects & n/a & 99.3% nAP object detection, 37 s assisted grasping, and 24 s active grasping \\ [PERSON] et al., 2024 [56] & Object detection for & & & & & 112,000 images from COCO, Open Images, LVIS, and Robolow Universe \\ [PERSON] et al., 2021 [53] & Multi-modal ADL recognition & TIAGo robot, RALT living-lab, and CNN-LSTM/TCN & RGB images and IMU data & Real test on adults & 16 healthy & 98.61\% action recognition on real data \\ [PERSON] et al., 2021 [57] & Robotic manipulation for assisted drinking & Kinova Jaco robotic arm, face, and dlib landmark detection & RGB images and distance sensory data & Real test on adults & 9 healthy & 99.54\% cup delivery success rate \\  & Smart home systems & & & & & & Participatory design \\ [PERSON] et al., 2021 [52] & for people with dementia and caregivers & Tablet-based puzzles, chatbots, smartwatches, ambient sensors, and physiological & Semi-structured interviews, focus and on older adults & Real test & 35 caregivers & processes foster more effective, inclusive, and rapid innovation in public health sectors \\ [PERSON] et al., 2019 [58] & Robotic manipulation & PR2 robot, 7-DoF robotic arm, amd ANN & Capacitive sensory data & Real test on adults & 4 healthy & <2.6 cm distance and <6 N applied force \\ [PERSON] et al., 2019 [54] & Walking support for elderly & ASTRO robot and decision trees & Laser scan data, FSR sensory data, and questionnaire & Real test on adults & 7 healthy & 94.2\% driving path accuracy and positive user experience \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Findings and characteristics of included AI and HCI studies for elderly support and AL. into therapeutic settings. Subsequently, [PERSON] et al. [61] investigated how lighting variations impact emotion recognition during HCI scenarios using VR and CNN-based models on EEG data, achieving a recognition rate of 41.63%. Their findings highlight the challenges of accurately detecting emotions in dynamic environments, an essential aspect of adaptive HCI systems. Finally, [PERSON] et al. [62] examined the role of virtual interactions in mitigating social anxiety, analyzing chatbot-based textual and vocal communication patterns in a longitudinal study with 618 undergraduate students. Their results indicate that frequent engagement in HCI reduces online social anxiety but not offline anxiety, while emotional expression plays a key role in lowering anxiety levels. Collectively, these studies underline the growing potential of AI-enhanced HCI for emotional support and social engagement. However, the relatively modest emotion recognition rates and the limited transferability of improvements from online to offline contexts point to key challenges. Future research should aim to improve emotion detection accuracy under real-world conditions and evaluate the long-term psychological impacts of sustained interaction with AI companions [63].