# Analysis of Physiological Parameters and Driver Posture for Prevention of Road Accidents: A Review

## TL;DR

This review examines methods for preventing road accidents by monitoring drivers' physiological states and posture using sensors and machine learning.

## Contribution

The paper provides a comprehensive quantitative analysis of existing systems for posture recognition and physiological monitoring across various applications.

## Key findings

- Most posture recognition systems detect basic movements like forward, backward, left, and right.
- Heart rate and respiration rate are the most commonly measured physiological parameters.
- Machine learning is widely used but challenges remain in detecting fine movements and measuring signals in noisy environments.

## Abstract

This review provides an overview of existing accident prevention methods by monitoring the persons’ physiological state, observing movements, and physiological parameters. Firstly, different physiological parameters monitoring systems are introduced. Secondly, various systems dealing with position recognition on pressure sensing mats are presented. We conduct an in-depth literature search and quantitative analysis of papers published in this area and focus independently of the application (drivers, office and wheelchair users, etc.). Quantitative information about the number of subjects, investigated scenarios, sensor types, machine learning usage, and laboratory vs. real-world works is extracted. In posture recognition, most works recognize at least forward, backward, left and right movements on a seat. The remaining works use the pressure sensing mat for bedridden people. In physiological parameters measurement, most works detect the heart rate and often also add respiration rate recognition. Machine learning algorithms are used in most cases and are taking on an ever-greater importance for classification and regression problems. Although all solutions use different techniques, returning satisfactory results, none of them try to detect small movements, which can pose challenges in determining the optimal sensor topology and sampling frequency required to detect fine movements. For physiological measurements, there are lots of challenges to overcome in noisy environments, notably the detection of heart rate, blood pressure, and respiratory rate at very low signal-to-noise levels.