  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.
