TExNet: long short-term fast fall detection based on attention enhancement and self-adaption

Falls pose a significant threat to human safety, making rapid and accurate detection and response essential. Time Exploration Network (TExNet), an attention-enhanced network tailored for identifying falls accurately and rapidly, is proposed in this paper. Unlike existing studies that rely on simulated environments, TExNet addresses the gap between simulated and real scenarios by integrating multi-branch timing and classification characteristics. It features a two-branch adaptive fusion framework, leveraging Convoluational Neural Network (CNN) and Transformer architectures to capture both local and global dependencies effectively. Additionally, dual-branch adaptive fusion framework incorporates dilated convolution and time series positional decomposition to enhance temporal correlation understanding. To handle data distribution variations, it employs an Invariant Risk Minimization (IRM) inspired loss function, penalizing misclassification of positive examples. This reduces model reliance on specific environments and improves action understanding. Moreover, a data self-conditioning module enhances data diversity and tackles imbalance issues. The model is deployed using a fine-tuning strategy based on a pre-trained framework combined with few-shot learning for downstream tasks. Experimental results show that the fine-tuned model achieves a recall of 92.16%, indicating its strong ability to rapidly adapt to new data distributions. Extensive experiments also validate the superiority of TExNet compared with existing approaches.