基于属性神经网络和上下文信息的人体活动识别
Human Activity Recognition using Attribute-Based Neural Networks and Context Information
摘要：
我们考虑在手动工作过程中从可穿戴传感器数据中识别人类活动（HAR），如仓库订单拣选。这样的结构化域通常可以被划分成不同的处理步骤，例如，包装或运输。每个过程步骤可以在活动类别上具有不同的先验分布，例如，站立或行走，以及不同的系统动力学。在这里，我们展示了如何将这些上下文信息系统地集成到基于深度神经网络的HAR系统中。具体来说，我们提出了一种混合架构，结合了一个深度神经网络，估计高层次的运动描述符，属性，从原始传感器数据和一个浅分类器，预测活动类从估计的属性和（可选的）上下文信息，如当前执行的过程步骤。我们的经验表明，我们提出的架构提高HAR性能，相比国家的最先进的方法。此外，我们表明，HAR性能可以进一步提高时，有关过程步骤的信息被纳入，即使该信息是只有部分正确的。
英文摘要：
We consider human activity recognition (HAR) from wearable sensor data in manual-work processes, like warehouse order-picking. Such structured domains can often be partitioned into distinct process steps, e.g., packaging or transporting. Each process step can have a different prior distribution over activity classes, e.g., standing or walking, and different system dynamics. Here, we show how such context information can be integrated systematically into a deep neural network-based HAR system. Specifically, we propose a hybrid architecture that combines a deep neural network-that estimates high-level movement descriptors, attributes, from the raw-sensor data-and a shallow classifier, which predicts activity classes from the estimated attributes and (optional) context information, like the currently executed process step. We empirically show that our proposed architecture increases HAR performance, compared to state-of-the-art methods. Additionally, we show that HAR performance can be further increased when information about process steps is incorporated, even when that information is only partially correct.
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。
