A Dataset for Multi-intensity Continuous Human Activity Recognition through Passive Sensing

TL;DR

This paper introduces mmDoppler, a comprehensive dataset using mmWave radar to capture both macro and micro-scale human activities, enhancing passive and continuous human activity recognition with adaptive doppler resolution.

Contribution

The paper presents mmDoppler, a novel dataset that combines macro and micro-scale activity data using adaptive doppler resolution in mmWave radar, filling a gap in HAR resources.

Findings

Enhanced recognition of micro-scale activities.

Adaptive doppler resolution improves activity detection.

Dataset includes detailed range-doppler heatmaps.

Abstract

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.