A Multi-Modal CNN-LSTM Framework with Multi-Head Attention and Focal Loss for Real-Time Elderly Fall Detection
arXiv:2603.22313v1 Announce Type: new Abstract: The increasing global aging population has intensified the demand for reliable health monitoring systems, particularly those capable of detecting critical events such as falls among elderly individuals. Traditional fall detection approaches relying on single-modality acceleration data suffer from high false alarm rates, while conventional machine learning methods require extensive hand-crafted feature engineering. This paper proposes a novel multi-modal deep learning framework, MultiModalFallDetector, designed for real-time elderly fall detection using wearable sensors. Our approach integrates multiple innovations: a multi-scale CNN-based feature extractor capturing motion dynamics at varying temporal resolutions; fusion of tri-axial accelerometer, gyroscope, and four-channel physiological signals; incorporation of a multi-head self-attention mechanism for dynamic temporal weighting; adoption of Focal Loss to mitigate severe class imbala
arXiv:2603.22313v1 Announce Type: new Abstract: The increasing global aging population has intensified the demand for reliable health monitoring systems, particularly those capable of detecting critical events such as falls among elderly individuals. Traditional fall detection approaches relying on single-modality acceleration data suffer from high false alarm rates, while conventional machine learning methods require extensive hand-crafted feature engineering. This paper proposes a novel multi-modal deep learning framework, MultiModalFallDetector, designed for real-time elderly fall detection using wearable sensors. Our approach integrates multiple innovations: a multi-scale CNN-based feature extractor capturing motion dynamics at varying temporal resolutions; fusion of tri-axial accelerometer, gyroscope, and four-channel physiological signals; incorporation of a multi-head self-attention mechanism for dynamic temporal weighting; adoption of Focal Loss to mitigate severe class imbalance; introduction of an auxiliary activity classification task for regularization; and implementation of transfer learning from UCI HAR to SisFall dataset. Extensive experiments on the SisFall dataset, which includes real-world simulated fall trials from elderly participants (aged 60-85), demonstrate that our framework achieves an F1-score of 98. 7, Recall of 98. 9, and AUC-ROC of 99. 4, significantly outperforming baseline methods including traditional machine learning and standard deep learning approaches. The model maintains sub- 50ms inference latency on edge devices, confirming its suitability for real-time deployment in geriatric care settings.
Executive Summary
This article presents a novel multi-modal deep learning framework, MultiModalFallDetector, for real-time elderly fall detection using wearable sensors. The proposed framework integrates multiple innovations, including a multi-scale CNN-based feature extractor, multi-head self-attention mechanism, Focal Loss, and transfer learning. Experimental results on the SisFall dataset demonstrate that the framework achieves state-of-the-art performance, significantly outperforming baseline methods. The model maintains sub-50ms inference latency on edge devices, confirming its suitability for real-time deployment in geriatric care settings. This study has significant implications for the development of reliable health monitoring systems for elderly individuals.
Key Points
- ▸ Proposes a novel multi-modal deep learning framework for elderly fall detection
- ▸ Integrates multiple innovations, including multi-scale CNN, multi-head self-attention, Focal Loss, and transfer learning
- ▸ Achieves state-of-the-art performance on the SisFall dataset with sub-50ms inference latency
Merits
Strength in Architecture
The proposed framework integrates multiple innovations, which enhances its ability to capture complex temporal and spatial patterns in wearable sensor data.
Improved Performance
The framework achieves state-of-the-art performance on the SisFall dataset, which demonstrates its effectiveness in real-world elderly fall detection scenarios.
Demerits
Limited Generalizability
The study is limited to the SisFall dataset, and its performance on other datasets and real-world scenarios remains unclear.
Computational Complexity
The proposed framework may require significant computational resources, which could limit its deployment on edge devices with limited resources.
Expert Commentary
The proposed framework presents a significant advancement in the field of elderly fall detection, leveraging the strengths of multi-modal deep learning techniques. However, its limitations, particularly in terms of generalizability and computational complexity, need to be addressed in future studies. Additionally, the study's findings have significant implications for the development of reliable health monitoring systems for elderly individuals, which could be integrated into geriatric care settings and public health policies.
Recommendations
- ✓ Future studies should investigate the performance of the proposed framework on other datasets and real-world scenarios to assess its generalizability.
- ✓ The development of standardized health monitoring systems for elderly individuals should be prioritized, incorporating the proposed framework and other innovative approaches.
Sources
Original: arXiv - cs.LG
