用热成像与注意力机制实现高精度无感跌倒检测。
Thermal Imaging-based Real-time Fall Detection using Motion Flow and Attention-enhanced Convolutional Recurrent Architecture
- 结合运动流与多注意力机制的双向卷积LSTM模型。
- 在TSF数据集上达99.7%的ROC-AUC，TF-66上表现稳健。
- 适合养老机构部署，无需用户配合且保护隐私。
老年人跌倒是一大公共健康问题。现有基于可穿戴设备、环境传感器和RGB视觉系统的解决方案在可靠性、用户依从性和实用性方面存在挑战。研究表明，老年群体及照护机构更倾向采用非可穿戴、被动式、隐私保护且实时的跌倒检测系统，且无需用户交互。本研究提出一种基于热成像的先进跌倒检测方法，采用增强型双向卷积长短期记忆（BiConvLSTM）模型，融合空间、时间、特征、自注意力和通用注意力机制。通过数百种模型变体的系统性实验，探索注意力机制、循环模块与运动流的集成效果，最终确定最优架构。其中，BiConvLSTM在TSF数据集上达到99.7%的ROC-AUC，并在新出现的多样化、隐私保护基准TF-66上表现出色。结果表明该模型具备强泛化能力与实用性，为热成像跌倒检测树立了新标准，推动高性能、可部署解决方案的发展。
原文摘要 · Abstract (English)
Falls among seniors are a major public health issue. Existing solutions using wearable sensors, ambient sensors, and RGB-based vision systems face challenges in reliability, user compliance, and practicality. Studies indicate that stakeholders, such as older adults and eldercare facilities, prefer non-wearable, passive, privacy-preserving, and real-time fall detection systems that require no user interaction. This study proposes an advanced thermal fall detection method using a Bidirectional Convolutional Long Short-Term Memory (BiConvLSTM) model, enhanced with spatial, temporal, feature, self, and general attention mechanisms. Through systematic experimentation across hundreds of model variations exploring the integration of attention mechanisms, recurrent modules, and motion flow, we identified top-performing architectures. Among them, BiConvLSTM achieved state-of-the-art performance with a ROC-AUC of $99.7\%$ on the TSF dataset and demonstrated robust results on TF-66, a newly emerged, diverse, and privacy-preserving benchmark. These results highlight the generalizability and practicality of the proposed model, setting new standards for thermal fall detection and paving the way toward deployable, high-performance solutions.
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