## 2 Related Works
### Artificial Intelligence in Elderly Care  
In elderly care, the application of AI has seen a transformative breakthrough, particularly through the deployment of sophisticated algorithms and neural network architectures for pose detection, fall detection, and activity monitoring. Human pose estimation [22; 23], serving as the core component of such automated analysis systems, lays the foundation for subsequent analyses like fall detection and activity monitoring. State-of-the-art algorithms such as PoseNet [24] and BlazePose [25] have revolutionized pose estimation with 3D and 2D image data using CNN architectures, significantly impacting research and practical applications in this field. These algorithms excel in accurately identifying and tracking human body positions in real time, which is critical for monitoring elderly behavior.  
Fall detection models rely heavily on object detection or pose estimation models. These models typically utilize a combination of spatial and temporal data processed through advanced machine learning algorithms like CNNs and Long Short-Term Memory networks (LSTMs) [26; 27]. The spatial aspect involves analyzing the posture and position of an individual, while the temporal component examines movement patterns over time. However, some models also use direct classification approaches using pose/skeleton data, as presented in [28; 29; 30], where the pose of a person is used to classify whether or not a fall has occurred. Moreover, other research incorporates object detection models and bounding box techniques for fall classification, as shown in [31; 32]. Recent research has employed diverse and innovative strategies [33; 34] to enhance the reliability and accuracy of fall detection, a critical component in ensuring the safety of elderly people.  
Pose estimation models have also led research in the direction of activity recognition. Recent advancements in machine learning (ML) allow these models to utilize skeletal information derived from pose estimation [35], coupled with spatial-temporal data and/or other robust features, to effectively classify human activities [17; 36]. As shown in the analysis presented in [6], vision-based activity recognition models have shown greater robustness compared to motion sensor-based monitoring systems. This is primarily due to the distinct visual features that can be separated into a variety of actions. In contrast, motion sensor features may display similar speeds and patterns across different actions, leading to potential ambiguities in classification. Therefore, several elderly monitoring systems utilize these models to automate the behavioral monitoring of elderly subjects [12]. Some research also combines pose data with handcrafted features to achieve fall detection classification, as presented in the method proposed in [37], where distances and angle features between pose keypoints between consecutive frames were used to detect falls. Similarly, in [38], other biometric features from pose keypoints were extracted and used to train a random forest model to classify falls.  
Despite the advancements in skeleton-based fall detection methods [39], there remain significant gaps in the technology, particularly in terms of privacy and user-centric approaches for elderly surveillance. Enhancing the privacy aspects of monitoring systems, for instance, does not just require adhering to ethical standards but also increasing the acceptance and comfort level of elderly individuals who are being monitored. Privacy-preserving methods, such as anonymization of video data, can foster greater trust and willingness among elderly users [40; 41; 42; 43; 44].