Human Activity Recognition (HAR) refers to a field that aims to identify human activities by adopting multiple techniques. In this field, different applications, such as smart homes and assistive robots, are introduced to support individuals in their Activities of Daily Living (ADL) by analyzing data collected from various sensors. Apart from wearable sensors, the adoption of camera frames to analyze and classify ADL has emerged as a promising trend for achieving the identification and classification of ADL. To accomplish this, the existing approaches typically rely on object classification with pose estimation using the image frames collected from cameras. Given the existence of inherent correlations between human–object interactions and ADL, further efforts are often needed to leverage these correlations for more effective and well justified decisions. To this end, this work proposes a framework where Graph Neural Networks (GNN) are adopted to explicitly analyze human–object interactions for more effectively recognizing daily activities. By automatically encoding the correlations among various interactions detected through some collected relational data, the framework infers the existence of different activities alongside their corresponding environmental objects. As a case study, we use the Toyota Smart Home dataset to evaluate the proposed framework. Compared with conventional feed-forward neural networks, the results demonstrate significantly superior performance in identifying ADL, allowing for the classification of different daily activities with an accuracy of 0.88. Furthermore, the incorporation of encoded information from relational data enhances object-inference performance compared to the GNN without joint prediction, increasing accuracy from 0.71 to 0.77.

中文翻译：

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采用图神经网络分析人与物体的交互以推断日常生活活动

人类活动识别（HAR）是指采用多种技术来识别人类活动的领域。在这一领域，引入了智能家居和辅助机器人等不同的应用程序，通过分析从各种传感器收集的数据来支持个人的日常生活活动（ADL）。除了可穿戴传感器之外，采用相机框架来分析和分类 ADL 已成为实现 ADL 识别和分类的一个有前景的趋势。为了实现这一目标，现有方法通常依赖于使用从相机收集的图像帧进行姿态估计的对象分类。鉴于人与物体交互和 ADL 之间存在固有的相关性，通常需要进一步努力来利用这些相关性来做出更有效和合理的决策。为此，这项工作提出了一个框架，采用图神经网络（GNN）来明确分析人与物体的交互，以更有效地识别日常活动。通过自动编码通过一些收集的关系数据检测到的各种交互之间的相关性，该框架推断出不同活动及其相应环境对象的存在。作为案例研究，我们使用丰田智能家居数据集来评估所提出的框架。与传统的前馈神经网络相比，结果显示在识别 ADL 方面具有显着优越的性能，能够以 0.88 的准确率对不同的日常活动进行分类。此外，与没有联合预测的 GNN 相比，结合来自关系数据的编码信息增强了对象推理性能，将准确度从 0.71 提高到 0.77。