## 1 Introduction  
Falls in the elderly are serious health problems with physical injuries, psychological effects, and social consequences. Falls can seriously impair the elderly's independence, increase medical costs, and lower their quality of life. According to the World Health Organization (WHO), approximately 28 to 35% of the population over the age of 65 experiences falls annually, often resulting in serious injuries or fatalities [1]. Consequently, the development of fall prevention and detection technologies is critical to ensuring the health and safety of the elderly [2].  
Despite recent advancements in fall detection technologies, conventional long short-term memory (LSTM)-based models often struggle to distinguish falls from similar activities of daily living (ADLs), as they treat all time steps uniformly [3; 4; 5; 6; 7; 8]. This limitation may result in the overlooking of critical motion cues, thereby increasing the likelihood of false positives and compromising system reliability in real-world applications.  
This study aims to address the challenge of accurate fall detection for seniors living alone by leveraging frequency-modulated continuous wave (FMCW) millimeter-wave (mmWave) radar sensors [9; 10]. These sensors offer distinct advantages over traditional methods, such as their privacy-preserving nature, robustness in varying lighting conditions, and ability to accurately capture human motion without physical contact, making them ideal for continuous and unobtrusive monitoring in private living spaces. The primary objective is to develop and evaluate robust fall detection algorithms that can effectively differentiate between various ADLs (walking, lying, inactivity, sitting) and actual falls. The scope of this research focuses on the application of x-y scatter and Doppler range images acquired from mmWave sensors.  
The novelty of this study lies in its comparative analysis of four distinct LSTM-based architectures for fall detection using FMCW mmWave radar data. While LSTM with attention mechanisms has been explored with wearable and vision-based sensors [11; 12; 13; 14], a comprehensive comparative study utilizing x-y scatter and Doppler range images from FMCW mmWave sensors has not been reported. Our research provides a detailed evaluation of how the integration of Histogram of Oriented Gradient (HOG) [15] features and an attention mechanism [16] enhances the accuracy and reliability of fall detection when applied to FMCW mmWave radar data. The identification of an optimal attention configuration further contributes to the practical implementation of such systems. The first model, the Raw-LSTM, processes unaltered mmWave radar images directly through LSTM layers. The second model, the Raw-LSTM-Attention, extended the Raw-LSTM by incorporating a self-attention mechanism within the traditional attention framework. The third model, the HOG-LSTM, enhances this by first extracting features using the HOG method [15] before passing them to the LSTM [17]. Building upon this, the fourth model, HOG-LSTM-Attention, integrates a self-attention mechanism to selectively focus on the most relevant temporal features within the attention framework [11; 12; 16], thereby enhancing detection accuracy.  
The paper is organized as follows: Section 2 describes firstly the experimental setup and sensor configuration, detailing the office environment and the placement of the radar sensor. Next, the subject and dataset used for the experiments are introduced, including the types of activities recorded and the characteristics of the participants. The feature extraction process using the HOG method is then explained, followed by a description of the LSTM networks and the attention mechanism employed in the study. The proposed fall detection algorithms are presented, along with the hyperparameters used for the models. Finally, the evaluation metrics and the results of the experiments are discussed, comparing the performance of different architectural models for fall detection.