Non-intrusive Driver Fatigue and Stress Monitoring Using Ambient Vibration Sensing

Source Organization:
University:Carnegie Mellon University
Principal Investigator:Hae Young Noh and Pei Zhang
PI Contact,
Project Manager:Courtney Ehrlichman
Funding Source(s) and Amounts Provided (by each agency or organization):$80,000
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Start and End Dates:January 2016 - January 2017
Project Status:Active
Subject Categories:In-Vehicle Technologies
Abstract:Autonomous vehicles holds tremendous promise in the near future. However, driver intervention will be needed in emergency or difficult situations. Therefore, real-time monitoring of the driver’s states, such as attention level, fatigue, and stress, is important to determine safety for transferring of control. In the past, many single-point on-body sensors and camera systems have been proposed, but these approaches are often limited to certain environments or require intrusive sensors on drivers that are difficult to deploy in reality. In this project, we will use inertial sensors embedded in the vehicle seat for recognizing driver’s physiological states (including movement, cardiovascular functions) and higher level states (including stress, fatigue, and attention level). Advantages of the inertial sensor based driver monitoring system come from its simple and non-intrusive nature, which makes deployment easy. With previous support from the University Transportation Center, we have developed a sensing platform consisting of a network of small vibrational sensors distributed inside car seats and seat belts that can successfully measure posture and breathing of drivers in noisy scenarios. Building on this hardware, we will continue our effort to develop data processing methods to extract detailed heart rate, heart rhythm, and movement of drivers. We will then use these vibrations to infer higher level human states, such as stress level, restlessness, and fatigue. The main challenge resides in high noise level due to the moving vehicle and sensing constraints relying only on contacts. To address these challenges, we plan to utilize multi-sourced, high-resolution and high frequency data with hybrid modeling approach to minimize uncertainties in signal processing and obtain reliable information. For this purpose, we will incorporate physical models of human body and vehicle dynamics in addition to data-driven models. We will continue working with our industry partner (Renault) to evaluate our system for laboratory and field testing.
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