# Real-Time Fall Monitoring for Seniors via YOLO and Voice Interaction
## 2 Related Work
### Fall Detection Technologies: A Classification Based on AI Paradigms
#### 2.1.1 System Classification by Computational Paradigms
Overall, classical machine learning systems offered significant improvements in adaptability and accuracy over rule-based methods, especially when trained with well-curated data. Deep learning (DL) methods have revolutionized fall detection by enabling systems to automatically learn complex spatiotemporal representations from raw data, eliminating the need for manual feature engineering. These systems commonly use models such as CNNs for spatial feature extraction, Long Short-Term Memory (LSTM) networks for capturing temporal dependencies, and increasingly, Transformer-based models that can process long sequences with attention mechanisms [21]. These models are applied across multiple data modalities, including inertial sensor streams (IMU data), video feeds, and audio recordings. In wearable sensor systems, deep learning models, especially 1D-CNNs and LSTM networks, are commonly applied to raw accelerometer and gyroscope data [22]. These architectures can detect subtle fall-related motion patterns and distinguish them from normal activities like walking, sitting, or transitioning [23]. Models like Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), or hybrid CNN-LSTM setups allow for capturing both local motion features and temporal dynamics across multiple time steps [24; 25]. Furthermore, Transformers, though more computationally intensive, have been explored for their ability to model long-range dependencies in sensor streams, offering a promising direction for continuous fall risk monitoring [26]. However, the performance of these systems is heavily dependent on the availability of annotated sensor datasets, which are limited in both size and diversity [27]. Most existing datasets feature scripted falls under controlled conditions, which may not reflect real-world behavior, particularly among elderly users with frailty or mobility impairments [21]. Despite their impressive capabilities, deep learning models are often viewed as \"black boxes,\" lacking transparent decision processes [28]. This poses challenges for clinical acceptance and user trust, particularly in eldercare settings. Moreover, deploying such models on real-time or edge systems requires model optimization (e.g., pruning, quantization) to ensure responsiveness without compromising accuracy [29]. Multimodal AI Systems  
Multimodal fall detection systems represent an advanced direction in monitoring technologies by simultaneously integrating multiple data streams from various sensor types, such as inertial sensors (IMUs), RGB and depth cameras, and microphones for detecting impact sounds, as well as contextual data like location, time, or user activity patterns [30]. This approach enables complex and comprehensive analysis, enhancing both detection accuracy and context awareness of the event. To process these heterogeneous data inputs, multimodal systems use sensor fusion architectures such as hybrid Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) models or multimodal transformers, which effectively combine temporal and spatial signals from different sources. These models allow not only the identification of a fall from a single data source but also cross-validation across multiple streams, significantly reducing false alarms, a common issue in single-channel systems [31]. However, several limitations hinder the widespread adoption of such systems:  
* _Increased complexity:_ Integrating and synchronizing data from multiple sensor modalities presents significant hardware and software challenges. * _Calibration and maintenance requirements_: Each sensor may necessitate individual calibration, and the entire system must maintain synchronized and stable performance over time, factors that increase costs and technical support demands. * _High resource consumption:_ Real-time processing of multimodal data streams requires substantial computational power and energy, potentially limiting deployment on resource-constrained edge devices. A relevant example is the study by [PERSON] et al.