## 5 Conclusions  
This study focused on developing and evaluating a fall detection system using mmWave radar technology. The experimental setup involved placing the radar sensor in an office environment to monitor human activities and detect falls. The radar sensor used captured _x-y_ scatter data and Doppler measurements to identify and classify different ADLs and falls. The dataset for the experiments included five activities: walking, lying, sitting, inactivity, and falls. The data was collected from seven participants, resulting in a total of 669 samples, including 285 falls and 384 ADLs. Key findings of this research highlight the superior performance of the HOG-LSTM-Attention model. The architecture, which combines HOG for feature extraction with a self-attention mechanism integrated into LSTM networks, consistently outperformed the Raw-LSTM, Raw-LSTM-Attention, and HOG-LSTM models across all evaluation metrics. Specifically, the HOG-LSTM-Attention model achieved an _Accuracy_ of 95.3%, _Sensitivity_ of 98.2%, _Precision_ of 92.9%, and an _F1-Score_ of 95.5%. The optimal self-attention configuration was identified as a 2:64 ratio of attention heads to channels for keys and queries, demonstrating the importance of fine-tuning these parameters for peak performance. These results affirm that combining robust feature engineering (HOG) with advanced temporal modeling (LSTM) and selective focus (attention) significantly enhances the accuracy and reliability of fall detection using mmWave radar. Despite these promising results, several limitations were encountered during this study. Firstly, the dataset, while diverse in activities, was collected from a relatively small number of participants (seven individuals aged 20-50). This demographic may not fully represent the diverse movement patterns and physical conditions of the elderly population (over 65 years old), who are the primary beneficiaries of such systems. Therefore, the generalizability of the model's performance to a broader, older demographic might be limited. Secondly, the experiments were conducted in a controlled office environment. Real-world living spaces can vary significantly in layout, presence of obstacles, and background clutter, which could affect radar signal propagation and detection accuracy. Thirdly, while the model showed high overall accuracy, distinguishing between 'lying' and 'falling' activities remained a challenge, as indicated by persistent misclassifications in the confusion matrices. This suggests that certain ADLs can still mimic fall patterns, leading to potential false alarms or missed detections. Future research should focus on expanding dataset diversity by including a wider range of participants, particularly older adults with varying body types and mobility profiles, to enhance model generalization across real-world populations. Additionally, testing in more realistic and heterogeneous home environments--with varied spatial layouts and interference sources--is essential to evaluate performance under operational conditions. The findings from this study strongly suggest that mmWave radar technology, when combined with advanced machine learning techniques like HOG feature extraction and attention-enhanced LSTM, can be effectively used for highly accurate and reliable fall detection. By addressing the identified limitations, this technology has the potential to significantly contribute to improved safety and independent living for vulnerable populations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Evaluation Metrics**} & \multicolumn{5}{c}{**NumHeads:NumKeyChannels**} \\ \cline{2-7}  & **1:32** & **2:64** & **4:128** & **8:256** & **16:512** & **32:1024** \\ \hline _Sensitivity_ [\%] & 92.7 & 98.2 & 98.2 & 94.5 & 94.5 & 76.4 \\ _Precision_ [\%] & 88.7 & 92.9 & 90.1 & 90.7 & 88.9 & 92.2 \\ _Accuracy_ [\%] & 90.4 & 95.3 & 93.7 & 92.4 & 91.4 & 85.0 \\ _F1-Score_ [\%] & 90.7 & 95.5 & 94.0 & 92.6 & 91.6 & 83.5 \\ \hline \hline \end{tabular}
\end{table}