## 2 Literature Review  
Various technologies for detecting falls have been developed among the elderly, each with its own advantages and limitations. Wearable devices, such as smartwatches and necklaces, often incorporate accelerometers and gyroscopes to detect sudden changes in body position and movement [3; 4; 5; 6; 18]. These sensors identify falls by measuring acceleration and rotational motion, respectively. Advanced systems use artificial intelligence (AI) and machine learning algorithms to improve accuracy and reduce false alarms [3; 4; 5; 6; 7; 8]. Medical alert systems like the Medical Guardian MGMini and MobileHelp Mobile Duo offer features such as Global Positioning System (GPS) tracking, automatic fall alerts, and 24/7 monitoring, providing immediate assistance in case of a fall [19]. Unwearable systems include camera-based solutions that use artificial intelligence (AI) algorithms to detect falls in real-time. These systems provide continuous monitoring without the use of wearable devices, making them ideal for wider regions or multiple patients [8; 20]. However, privacy concerns and the need for constant surveillance are significant drawbacks. Pressure sensor mats, placed on the floor, detect falls by sensing changes in pressure and are often used in combination with other systems to enhance detection accuracy [21]. In addition, hybrid approaches that integrate fall detection systems and smart home technologies are emerging [3; 4; 5]. These systems combine sensors, cameras, and wearable devices to provide comprehensive monitoring. For example, smartwatches can be paired with home security systems to alert caregivers or emergency services in case of a fall. Predictive analytics is another advanced feature in which systems analyze data from various sensors to identify fall risk patterns and prevent accidents before they happen. Emerging technologies such as FMCW mmWave sensors are gaining attention for their high resolution and non-contact fall detection capabilities [9; 10; 21; 22; 23; 24; 25; 26]. These sensors can penetrate clothing and other materials, providing reliable data without the need for direct contact, which addresses privacy concerns associated with camera-based systems and the inconvenience of wearable devices [9; 10]. Their ability to operate in various environmental conditions (e.g., darkness, fog) and provide precise velocity, range, and angle measurements makes them particularly suitable for robust fall detection applications [9; 10]. AI and machine learning algorithms are increasingly integrated with fall detection technologies, analyzing complex patterns in sensor data to improve accuracy and reliability. These different approaches highlight ongoing efforts to address the problem of fall detection in the elderly. Each technology offers unique advantages and faces certain challenges, and continuous re-research aims to improve its effectiveness and usability. The HOG method is widely recognized for its effectiveness in object detection and recognition tasks, owing to its ability to capture the shape and appearance of objects through the distribution of intensity gradients [15]. In the domain of fall detection using mmWave radar images, HOG features are particularly advantageous, as they effectively represent the spatial patterns and structural changes associated with various human activities and fall events [27]. By analyzing gradient magnitudes and orientations within localized regions of radar images, HOG can extract robust features that are resilient to minor variations in position, scale, or illumination. This robustness enables models to identify characteristic outlines and motion dynamics of individuals, thereby enhancing the discriminative capability for distinguishing falls from ADLs [27]. Hence, various deep learning architectures have been introduced to take advantage of the rich spatial and temporal information provided by FMCW mmWave sensors.