# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 2 Related Work  
A review of the relevant literature on fall detection and related technologies reveals a rapidly evolving landscape marked by advancements in sensor technology, machine learning algorithms, and telecommunication systems [39]. Researchers and practitioners alike are increasingly recognizing the importance of early fall detection in mitigating the adverse consequences of falls among the elderly population. Recent approaches from the latest studies in this field are illustrated in Figure 1.  
_Sensor Technologies for Fall Detection:_ Accelerometers and gyroscopes, which are inertial sensors, are commonly used for fall detection due to their ability to measure motion and orientation changes, with research indicating their effectiveness in accurately detecting falls while minimizing false alarms [40; 41]. Additionally, pressure sensors, including floor-based pressure sensors and wearable pressure-sensitive mats, have shown promise in detecting falls by identifying abrupt changes in pressure distribution upon impact with the ground [42]. Furthermore, depth-sensing cameras, such as Microsoft Kinect, have been explored for fall detection by analyzing skeletal movements and changes in body posture, offering the potential for accurate fall detection in home environments [9; 43]. _Machine Learning and Deep Learning Algorithms:_ Support Vector Machine (SVM) classifiers have been widely used for fall detection, demonstrating high accuracy in distinguishing between fall and non-fall events based on sensor data features [44]. Additionally, Artificial Neural Network (ANN) models have shown promising results in fall detection by learning complex patterns from sensor data, with deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) also being applied successfully. Furthermore, ensemble learning methods, such as random forests and gradient boosting, have emerged as effective techniques for improving fall detection performance by combining multiple classifiers [45; 46]. _Telecommunication Systems:_ Smartphones, with their built-in sensors and communication capabilities, offer a convenient platform for fall detection applications. Researchers  
Figure 1: Recent approaches to fall detection. have explored various smartphone-based solutions, including apps and wearable accessories, for continuous monitoring and emergency alerting [47]. _Wearable Devices_: Similarly, smartwatches and wearable devices equipped with accelerometers and other sensors have gained traction for fall detection among older adults, offering the advantage of continuous monitoring and seamless integration into daily routines [48]. _Challenges and Future Directions_: As fall detection technologies become more pervasive, ensuring user privacy and addressing ethical concerns surrounding data collection and sharing remain paramount. Engaging end-users, including older adults and caregivers, in the design and evaluation of fall detection systems is essential to ensure usability, acceptance, and long-term adoption [42]. Furthermore, additional research is required to validate the efficacy of fall detection technologies in real-world settings and diverse populations, considering factors such as environmental variability and user mobility [46]. In previous approaches to fall detection, several key issues and challenges have been identified, which have prompted ongoing research and innovation in the field. Here, we summarize some of the main issues along with references to relevant studies:  
* _False Alarms and Sensitivity_: Many fall detection systems suffer from high rates of false alarms, leading to user frustration and reduced system reliability [11]. * _Environmental Variability_: Fall detection systems may struggle to perform reliably in diverse environmental conditions, such as different lighting, flooring surfaces, or room layouts [49].