Abstract

The paper describes highly-sensitive passive electric potential sensors (EPS) for non-contact detection of multiple biophysical signals, including electrocardiogram (ECG), respiration cycle (RC), and electroencephalogram (EEG). The proposed EPS uses an optimized transimpedance amplifier (TIA), a single guarded sensing electrode, and an adaptive cancellation loop (ACL) to maximize sensitivity (DC transimpedance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$= 150\,\,\text{G}\Omega $ </tex-math></inline-formula> ) in the presence of power line interference (PLI) and motion artifacts. Tests were performed on healthy adult volunteers in noisy and unshielded indoor environments. Useful sensing ranges for ECG, RC, and EEG measurements, as validated against reference contact sensors, were observed to be approximately 50 cm, 100 cm, and 5 cm, respectively. ECG and RC signals were also successfully measured through wooden tables for subjects in sleep-like postures. The EPS were integrated with a wireless microcontroller to realize wireless sensor nodes capable of streaming acquired data to a remote base station in real-time.

Highlights

  • C ONTACT-based wearable physiological sensors, such as skin-coupled electrocardiogram (ECG) monitors, respiration belts, electroencephalogram (EEG) headbands, and smart watches [1], offer great promise for personalized healthcare [2], [3]

  • Contact sensors can interfere with natural sleep and bias the results of sleep studies [7]

  • Data was acquired by a USBbased data acquisition system (DAQ) (USB-1608-FS, Measurement Computing) connected to a battery-powered personal computer (PC) to minimize power line interference (PLI)

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Summary

Introduction

C ONTACT-based wearable physiological sensors, such as skin-coupled electrocardiogram (ECG) monitors, respiration belts, electroencephalogram (EEG) headbands, and smart watches [1], offer great promise for personalized healthcare [2], [3]. Such contact-based sensing systems generally provide good signal-to-noise ratio (SNR) due to direct contact with the monitored subject, reducing the interface circuit complexity. They suffer from challenges during long-term use due to issues with patient comfort, security and privacy, engagement and interaction, and psychological burden [4], [5].

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