Non-Intrusive Contact Respiratory Sensor for Vehicles

Author:

Meteier QuentinORCID,Kindt Michiel,Angelini LeonardoORCID,Abou Khaled OmarORCID,Mugellini Elena

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.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference31 articles.

1. Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles https://www.sae.org/standards/content/j3016_201806/

2. Designing an AI-Companion to Support the Driver in Highly Autonomous Cars. HCI https://link.springer.com/chapter/10.1007/978-3-030-49062-1_23

3. Controlled inducement and measurement of drowsiness in a driving simulator

4. A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability

5. Applying neural network analysis on heart rate variability data to assess driver fatigue

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