## 2 Related Work  
### Fall Detection with Wearable and Body Sensors  
Fall detection has witnessed the development of various techniques that utilize wearable and body sensors to detect falls accurately. For example, the authors of [17] proposed an advanced version of Long Short-Term Memory (LSTM) called Cerebral LSTM, demonstrating improved training and testing accuracy, particularly in time-series prediction. By collecting sensor data from the watch included with Microcontroller Units (MCUs) and utilizing the MobiFall dataset for training and testing validation, they achieved a fall-detection accuracy of approximately 98%. The authors of [18] presented a low-complexity deep learning model for fall detection, implemented on a microcontroller with limited memory resources. The microcontroller was deployed within a low-power, wide-area network with long-range (LoRa) communication technology. A comparative analysis of various lightweight neural networks and conventional machine learning algorithms demonstrated that the convolutional neural network (CNN) exhibited the highest accuracy of 95.55%. The authors of [19] presented mmFall, a real-time fall-detection system that utilizes millimeter-wave signals and accomplishes remarkable accuracy while maintaining a low computational complexity. They proposed a spatial-temporal processing method for extracting signal variation related to human activity. They used various data augmentation techniques, such as shifting and interpolating the signals to improve the efficacy and robustness of the system. In addition, they present a lightweight convolutional neural network architecture designed specifically for fall detection in real time. The authors demonstrate through extensive experiments that their proposed system obtains a state-of-the-art performance with minimal computational complexity. The authors of [20] developed the Patch-Transformer Network (PTN). This novel approach integrates convolutional layers for local feature extraction with transformer-encoding layers to capture global dependencies through self-attention mechanisms. This dual-layer architecture allows for a nuanced understanding of spatiotemporal features, crucial for detecting the complex dynamics of falls. The PTN has been tested on well-known public datasets such as SisFall and UnMib SHAR, achieving exceptionally high accuracies of 99.86% and 99.14%, respectively. This demonstrates the network's capability to efficiently process sensor data with high precision while maintaining low computational demands, making it ideal for real-time applications. The authors of [21] introduced an enhanced ensemble deep neural network approach tailored for elderly fall detection using wearable sensors. Their framework combines the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to create a robust feature extraction and temporal analysis system. The CNN component is responsible for extracting salient features from accelerometer and gyroscope data, while the RNN component models the temporal dynamics of the falling process. This combination allows for a nuanced differentiation between fall, prefall, and non-fall events, with accuracies reaching 95%, 96%, and 98%, respectively. Their ensemble model, which employs separate neural networks for each event class, provides an adaptive approach that enhances accuracy and processing speed across diverse scenarios. The authors of [22] conducted a feasibility study on real-time fall detection using wearable sensors and deep learning methods. Their research emphasizes the importance of data segmentation and sensor placement, two critical factors in the performance of fall-detection systems. They explored various deep learning architectures, including convolutional layers combined with LSTM and transformer blocks, to handle different aspects of sensor data analysis.