Indoor Activity and Vital Sign Monitoring for Moving People with Multiple Radar Data Fusion

Xiuzhu Yang,Xinyue Zhang,Yi Ding,Lin Zhang

doi:10.3390/rs13183791

Abstract

The monitoring of human activity and vital signs plays a significant role in remote health-care. Radar provides a non-contact monitoring approach without privacy and illumination concerns. However, multiple people in a narrow indoor environment bring dense multipaths for activity monitoring, and the received vital sign signals are heavily distorted with body movements. This paper proposes a framework based on Frequency Modulated Continuous Wave (FMCW) and Impulse Radio Ultra-Wideband (IR-UWB) radars to address these challenges, designing intelligent spatial-temporal information fusion for activity and vital sign monitoring. First, a local binary pattern (LBP) and energy features are extracted from FMCW radar, combined with the wavelet packet transform (WPT) features on IR-UWB radar for activity monitoring. Then the additional information guided fusing network (A-FuseNet) is proposed with a modified generative and adversarial structure for vital sign monitoring. A Cascaded Convolutional Neural Network (CCNN) module and a Long Short Term Memory (LSTM) module are designed as the fusion sub-network for vital sign information extraction and multisensory data fusion, while a discrimination sub-network is constructed to optimize the fused heartbeat signal. In addition, the activity and movement characteristics are introduced as additional information to guide the fusion and optimization. A multi-radar dataset with an FMCW and two IR-UWB radars in a cotton tent, a small room and a wide lobby is constructed, and the accuracies of activity and vital sign monitoring achieve 99.9% and 92.3% respectively. Experimental results demonstrate the superiority and robustness of the proposed framework.

Highlights

With the rapid development of the Internet of Things, remote human sensing has received considerable attention for health-care applications
The multi-radar dataset for activity and vital sign monitoring is constructed with an Frequency Modulated Continuous Wave (FMCW) and two Impulse Radio Ultra-Wideband (IR-UWB) radars
The IR-UWB radar is a System on Chip (SoC) with a built-in transmitter and receiver, based on the XeThru X4M03 chip produced by Novelda AS, Oslo, Norway

Summary

Introduction

With the rapid development of the Internet of Things, remote human sensing has received considerable attention for health-care applications. Measuring humans’ activity and physiological information is crucial for diverse remote monitoring scenarios, ranging from elder fall detection to patient monitoring [1,2]. Various sensors have been applied for remote health-care. Compared with contact devices, such as electrocardiogram (ECG) and photoplethysmograph (PPG), non-contact sensing avoids inconvenience and discomfort, and provides daily health monitoring. Non-contact sensors are mainly classified as vision-based, infrared-based and radio-based. Vision-based sensors suffer from insufficient illumination and raise privacy concerns, while the infrared-based solutions are temperature-sensitive. Radar systems are not affected by light and temperature conditions, leveraging reflected signals from the human body to analyze the activity and vital sign information. Radar systems show outstanding performance in personnel recognition [4], people counting [5], gait

Methods

Results

Conclusion