通过多模态深度学习识别日常活动：一种面向环境辅助养老的视频、姿态和物体感知方法
Recognition of Daily Activities through Multi-Modal Deep Learning: A Video, Pose, and Object-Aware Approach for Ambient Assisted Living
AI总结：
本文提出一种多模态深度学习方法，结合视频、姿态和物体信息，用于老年人日常活动识别，提升环境辅助养老系统的准确性与实用性。
摘要：
日常活动识别是有效环境辅助养老（AAL）系统的关键要素，特别是用于监控老年人福祉并支持其在室内环境中的独立性。然而，开发稳健的活动识别系统面临重大挑战，包括类别内变异性、类别间相似性、环境变异性、摄像机视角和场景复杂性。本文提出了一种针对老年人在AAL环境中的日常活动识别的多模态方法。所提出的系统整合了由3D卷积神经网络（CNN）处理的视觉信息，以及由图卷积网络分析的3D人体姿态数据。从物体检测模块获得的上下文信息通过交叉注意力机制与3D CNN特征融合，以提高识别准确性。该方法使用丰田SmartHome数据集进行评估，该数据集包含真实世界室内外活动。结果表明，所提出系统在多种日常活动上的分类准确性具有竞争力，突显了其作为高级AAL监控解决方案重要组成部分的潜力。这一进展支持了开发智能系统以促进老年人安全和自主性的更广泛目标。
英文摘要：
Recognition of daily activities is a critical element for effective Ambient Assisted Living (AAL) systems, particularly to monitor the well-being and support the independence of older adults in indoor environments. However, developing robust activity recognition systems faces significant challenges, including intra-class variability, inter-class similarity, environmental variability, camera perspectives, and scene complexity. This paper presents a multi-modal approach for the recognition of activities of daily living tailored for older adults within AAL settings. The proposed system integrates visual information processed by a 3D Convolutional Neural Network (CNN) with 3D human pose data analyzed by a Graph Convolutional Network. Contextual information, derived from an object detection module, is fused with the 3D CNN features using a cross-attention mechanism to enhance recognition accuracy. This method is evaluated using the Toyota SmartHome dataset, which consists of real-world indoor activities. The results indicate that the proposed system achieves competitive classification accuracy for a range of daily activities, highlighting its potential as an essential component for advanced AAL monitoring solutions. This advancement supports the broader goal of developing intelligent systems that promote safety and autonomy among older adults.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
