Human activity recognition (HAR) is essential in healthcare, elder care, security, and human-computer interaction. The use of precise sensor data to identify activities passively and continuously makes HAR accessible and ubiquitous. Specifically, millimeter wave (mmWave) radar is promising for passive and continuous HAR due to its ability to penetrate non-metallic materials and provide high-resolution wireless sensing. Although mmWave sensors are effective at capturing macro-scale activities, like exercising, they fail to capture micro-scale activities, such as typing. In this paper, we introduce mmDoppler, a novel dataset that utilizes off-the-shelf (COTS) mmWave radar in order to capture both macro and micro-scale human movements using a machine-learning driven signal processing pipeline. The dataset includes seven subjects performing 19 distinct activities and employs adaptive doppler resolution to enhance activity recognition. By adjusting the radar's doppler resolution based on the activity type, our system captures subtle movements more precisely. mmDoppler includes range-doppler heatmaps, offering detailed motion dynamics, with data collected in a controlled environment with single as well as multiple subjects performing activities simultaneously. The dataset aims to bridge the gap in HAR systems by providing a more comprehensive and detailed resource for improving the robustness and accuracy of mmWave radar activity recognition.

翻译：人体活动识别（HAR）在医疗保健、老年护理、安防以及人机交互等领域至关重要。利用精确的传感器数据进行被动、连续的活动识别，使得HAR技术变得易于获取且无处不在。具体而言，毫米波（mmWave）雷达因其能够穿透非金属材料并提供高分辨率无线感知能力，在被动、连续的HAR应用中展现出巨大潜力。尽管毫米波传感器能有效捕获宏观尺度的活动（如运动锻炼），却难以捕捉微观尺度的活动（例如打字）。本文介绍了mmDoppler——一个新颖的数据集，它利用商用现货（COTS）毫米波雷达，通过机器学习驱动的信号处理流程，同时捕获宏观与微观尺度的人体动作。该数据集包含七名受试者执行的19种不同活动，并采用自适应多普勒分辨率以提升活动识别性能。通过根据活动类型调整雷达的多普勒分辨率，我们的系统能更精确地捕捉细微动作。mmDoppler提供包含详细运动动态信息的距离-多普勒热图，数据采集于受控环境中，涵盖单人与多人同时执行活动的情景。该数据集旨在通过提供更全面、更详尽的资源来改善毫米波雷达活动识别的鲁棒性与准确性，从而弥补现有HAR系统的不足。