Wireless Sensors System for Stress Detection by Means of ECG and EDA Acquisition.

Antonio Affanni

doi:10.3390/s20072026

Abstract

This paper describes the design of a two channels electrodermal activity (EDA) sensor and two channels electrocardiogram (ECG) sensor. The EDA sensors acquire data on the hands and transmit them to the ECG sensor with wireless WiFi communication for increased wearability. The sensors system acquires two EDA channels to improve the removal of motion artifacts that take place if EDA is measured on individuals who need to move their hands in their activities. The ECG channels are acquired on the chest and the ECG sensor is responsible for aligning the two ECG traces with the received packets from EDA sensors; the ECG sensor sends via WiFi the aligned packets to a laptop for real time plot and data storage. The metrological characterization showed high-level performances in terms of linearity and jitter; the delays introduced by the wireless transmission from EDA to ECG sensor have been proved to be negligible for the present application.

Highlights

During recent years, the interest of scientific literature in the measurement of the stress in individuals has increased exponentially
Even if our main research activity focuses on mental stress detection of drivers at the simulator, the sensor developed in the present paper can be applied to other stressing conditions, for example in the literature we find application of electrodermal activity (EDA) and ECG to human-robot interaction [30], and to stress detection in the workplace [31,32]
In this work we describe the design of a low cost sensor for mental stress detection, measuring two EDA channels on the hands and two ECG channels on the chest, using a completely wireless transmission from the hands to the chest and from the chest to a laptop; in this way, data packets are always aligned since they are sent from a unique sensor

Summary

Introduction

The interest of scientific literature in the measurement of the stress in individuals has increased exponentially. The mental stress of the driver seems to be poorly correlated to pupil diameter or fixation duration [13] and, the eye-tracking systems need complex and accurate calibration on each subject under test. For these reasons, currently the majority of scientific papers focusing on driver mental stress detection rely on the measurement of bio-signals

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Discussion

Conclusion