Abstract

In this work, we propose a low-cost solution capable of collecting the driver’s respiratory signal in a robust and non-intrusive way by contact with the chest and abdomen. It consists of a microcontroller and two piezoelectric sensors with their respective 3D printed plastic housings attached to the seat belt. An iterative process was conducted to find the optimal shape of the sensor housing. The location of the sensors can be easily adapted by sliding them along the seat belt. A few participants took part in three test sessions in a driving simulator. They had to perform various activities: resting, deep breathing, manual driving, and a non-driving-related task during automated driving. The subjects’ breathing rates were calculated from raw data collected with a reference chest belt, each sensor alone, and the fusion of the two. Results indicate that respiratory rate could be assessed from a single sensor located on the chest with an average absolute error of 0.92 min−1 across all periods, dropping to 0.13 min−1 during deep breathing. Sensor fusion did not improve system performance. A 4-pole filter with a cutoff frequency of 1 Hz emerged as the best option to minimize the error during the different periods. The results suggest that such a system could be used to assess the driver’s breathing rate while performing various activities in a vehicle.

Highlights

  • Sensors 2022, 22, 880. https://The driver’s condition can have a direct impact on his or her ability to operate the vehicle

  • This paper proposes an innovative and low-cost solution to measure driver respiration rate by a contact-based method using two force-sensitive resistors attached to the seat belt by a specially designed housing

  • The system was evaluated with a few test subjects during different activities in a driving simulator: resting, deep breathing, manual driving, and non-driving activity during automated driving

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Summary

Introduction

Sensors 2022, 22, 880. https://The driver’s condition can have a direct impact on his or her ability to operate the vehicle. Conditional automated driving (L3-SAE) could be adopted on public roads, depending on technological and legislative advances If so, this type of vehicle will take over the dynamic driving task and ask drivers to regain control if necessary. One way to do this is to collect data that continuously assesses the driver’s condition and to use this information to provide optimal assistance. Physiological signals are one source of data providing intrinsic information about the driver’s condition The relevance of these to assess the state of the driver according to various components such as fatigue and drowsiness [3,4,5,6,7,8], workload [8,9,10,10,11,12,13] or stress [8,14,15,16,17] has been proven in scientific literature

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