# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 2 Related Work
### Fall Detection Technologies: A Classification Based on AI Paradigms
#### 2.1.1 System Classification by Computational Paradigms  
This development can be generally categorized into four computational paradigms. Each has distinct technological foundations, advantages, and limitations [10]:  
(a) Rule-Based Systems  
Rule-based systems represent the earliest generation of fall detection solutions. These systems typically operate using predefined threshold conditions applied to sensor data, most commonly from wearable inertial measurement units (IMUs) such as accelerometers and gyroscopes [11]. A typical detection rule might identify a fall if a sudden spike in acceleration is followed by a period of inactivity [12]. These approaches are straightforward to implement and can operate on low-power, embedded platforms. These systems depend heavily on user adherence, as the wearable device needs to be continuously worn and properly placed to operate effectively. Incorrect placement (e.g., rotated on the wrist or worn loosely) can significantly degrade performance. Furthermore, studies [13] have shown that threshold-based fall detectors often misinterpret normal daily activities, such as sitting abruptly, bending, or lying down voluntarily, as potential falls, especially in elderly users with unsteady gait or frailty [14]. Although ambient rule-based systems are advantageous for being non-intrusive and not needing any wearable devices or user engagement, they heavily rely on location and are limited to the specific zones where sensors are placed. Their rigid decision rules can also result in false negatives, particularly if a fall occurs outside the monitored area or if noise or lighting conditions interfere with sensor performance. Moreover, calibration is often required for each physical space, making them difficult to scale in dynamic or large environments [15]. (b) Classical Machine Learning Systems  
Classical machine learning (ML) systems marked a major advancement over rule-based methods by enabling data-driven learning and greater flexibility in modeling. These systems apply algorithms such as Support Vector Machines (SVMs), k-Nearest Neighbors (k-NNs), Decision Trees, and Random Forests to features manually extracted from raw sensor data or video streams [16]. Common features include jerk, postural angles, velocity changes, energy consumption, and temporal sequences of motion states [17]. In wearable-based systems, ML is typically applied to inertial sensor data, such as acceleration and angular velocity, collected from IMUs attached to the body (e.g., wrist, waist, ankle). Feature engineering in this context may include both time-domain (e.g., signal variance, mean crossing rate) and frequency-domain metrics (e.g., dominant frequency, Fast Fourier Transform components) for capturing meaningful aspects of human motion indicative of a fall. However, one of the major drawbacks in wearable ML systems is the user dependence and variability of sensor data. The same movement can produce very different signals across individuals due to differences in gait, body composition, or sensor positioning. Thesystems remain susceptible to false positives, especially when routine activities like quickly sitting, stepping down, or reaching are interpreted as falls [3; 18]. In vision-based systems, classical ML methods rely on descriptors derived from handcrafted visual features, such as bounding box trajectories, silhouette shapes, or skeletal keypoint dynamics. Classifiers such as SVMs or k-NN often receive these descriptors. For example, a rapid transition from a vertical to a horizontal bounding box, followed by inactivity, could be interpreted as a fall [19]. Yet, as with wearable applications, vision-based ML suffers from sensitivity to environmental variations, including lighting conditions, camera angles, and occlusions. Feature extraction pipelines must often be recalibrated for each deployment scenario to maintain acceptable accuracy [20].