MIMOGR:MIMO millimeter wave radar multi-feature dataset for gesture recognition

Radar-based dynamic gesture recognition has a broad prospect in the field of touchless Human-Computer Interaction (HCI) due to its advantages in many aspects such as privacy protection and all-day working. Due to the lack of complete motion direction information, it is difficult to implement existing radar gesture datasets or methods for motion direction sensitive gesture recognition and cross-domain (different users, locations, environments, etc.) recognition tasks. Therefore, this paper constructs a sensitive feature map dataset of RT, DT, ART, ERT and RDT based on the range velocity, altitude and azimuth information of MIMO millimeter wave radar. A total of 7 types of gestures can be collected, namely waving up, waving down, waving left, waving right, waving forward, waving backward, and double-tap. To prepare a realistic dataset, we employ two data collection strategies: collecting gesture samples from various volunteers and collecting gesture data in various scenarios to enrich our sample.基于雷达的动态手势识别在非接触式人机交互（Human-Computer Interaction, HCI）领域拥有广阔应用前景，因其在隐私保护、全天候作业等诸多维度具备显著优势。由于缺乏完整的运动方向信息，现有雷达手势数据集及相关方法难以适用于对运动方向敏感的手势识别任务，以及跨域（不同用户、采集位置、应用环境等）识别任务。为此，本文基于多输入多输出（Multiple-Input Multiple-Output, MIMO）毫米波雷达的距离、速度、高度及方位信息，构建了涵盖RT、DT、ART、ERT及RDT的敏感特征图数据集。该数据集共涵盖7类手势，具体包括向上挥手、向下挥手、向左挥手、向右挥手、向前挥手、向后挥手与双击。为构建贴近真实应用场景的数据集，我们采用了两种数据采集策略：面向不同志愿者采集手势样本，并在多样化场景下采集手势数据以扩充样本规模。

搜集汇总

背景与挑战

背景概述

该数据集是一个基于MIMO毫米波雷达的多特征手势识别数据集，包含7种手势类型和两种数据收集策略（志愿者基础和场景基础），旨在解决手势识别中的运动方向敏感性和跨领域识别问题。数据集格式为.npy文件，包含五种不同的特征表示。