A Radar-Based Human Activity Recognition Using a Novel 3-D Point Cloud Classifier
A Radar-Based Human Activity Recognition Using a Novel 3-D Point Cloud Classifier
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DOI：
10.1109/jsen.2022.3198395
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发表时间：
2022-10
影响因子：
4.3
通讯作者：
Zheqi Yu;Ahmad Taha;William Taylor;A. Zahid;Khalid Rajab;H. Heidari;M. Imran;Q. Abbasi
中科院分区：
文献类型：
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作者：
Zheqi Yu;Ahmad Taha;William Taylor;A. Zahid;Khalid Rajab;H. Heidari;M. Imran;Q. Abbasi
本文为三维点云分类提供了一个新的基准数据集，其中手动标记的人类活动数据超过每帧100个点云，并且能够满足数据密集型学习方法的训练需求。在这项研究中，考虑了一个案例研究，用于使用深度长短期记忆（LSTM）神经网络评估基准，该网络在最先进的人类活动识别（HAR）领域表现出显着的性能改进。迄今为止，许多类型的收集装置已被用于识别人类活动。然而，由于训练数据的稀缺，三维点云标注任务尚未取得重大进展。为了克服这一挑战，我们的目标是推导出这一数据需求差距，使深度学习方法在3D点云任务中充分发挥潜力。用于此过程的数据集由NodeNs公司支持的多输入多输出（MIMO）雷达（NodeNs ZERO 60 GHz IQ雷达）使用静态地面传感器采集的密集点云组成。它包含从一到四个人的多种类型的人类数据，并包含一系列人类动作场景，包括站立，坐着，拿起，摔倒和行走。此外，它还调查了传感器位置和从单个受试者到多个受试者的人类数据收集要求，以及识别和分析了收集活动数据的各种传感设备和应用程序。在这方面，对几个基准数据集进行了彻底的研究，检查传感器，特征，活动类别和其他数据。最后，在现有研究的基础上，对几种基准数据集上的活动识别方法进行了比较和分析。与现有设备不同，新的NodeNs传感器提供更容易访问和直接的点云数据来捕获人体运动信息。依靠先进的检测算法来处理点云数据，它在基准数据集上达到了95%以上的准确率。
This article provides a new benchmark dataset for 3-D point cloud classification in which the manually labeled human activity data exceeds 100 point clouds per frame and is capable of meeting the training needs for data-intensive learning approaches. In this study, a case study is considered for evaluating the benchmark using a deep long short-term memory (LSTM) neural network, which demonstrated a significant performance improvement over the state-of-the-art human activity recognition (HAR) area. To date, numerous types of collection devices have been used in the recognition of human activities. However, due to the scarcity of training data, the task of 3-D point cloud labeling has not yet made significant progress. To overcome this challenge, it is aimed to deduce this data requirements gap, allowing deep-learning methods to reach their full potential in 3-D point cloud tasks. The dataset used for this process is comprised of dense point clouds acquired with the static ground sensor by the NodeNs company-supported multiple input multiple output (MIMO) radar (NodeNs ZERO 60 GHz IQ radar). It contains multiple types of human being data ranging from one to four individuals and encompasses a range of human action scenarios, including standing, sitting, picking up, falling, and walking. Furthermore, it also investigated sensor locations and requirements for human being data collection that is from a single subject to multiple subjects, as well as identified and analyzed various sensing devices and applications that collect activity data. In this regard, a thorough study is conducted on several benchmark datasets, examining sensors, characteristics, activity categories, and other data. Finally, it compares and analyzes the activity recognition methods used in several benchmark datasets based on the current study. Unlike existing devices, the new NodeNs sensor provides more accessible and straightforward point cloud data to capture human movement information. Depending on an advanced detection algorithm to process point cloud data, it achieved more than 95% accuracy on the benchmark dataset.