Wearable Glove-Type Driver Stress Detection Using a Motion Sensor

Boon Giin Lee, Wan Young Chung

Research output: Journal PublicationArticlepeer-review

37 Citations (Scopus)

Abstract

Increased driver stress is generally recognized as one of the major factors leading to road accidents and loss of life. Even though physiological signals are reported as the most reliable means to measure driver stresses, they often require the use of unique and expensive sensors, which produce dynamic and varying readings within individuals. This paper presents a novel means to predict a driver's stress level by evaluating the movement pattern of the steering wheel. This is accomplished by using an inertial motion unit sensor, which is placed on a glove worn by the driver. The motion sensor selected for this paper was chosen because for its low cost and the fact that it is least affected by environmental factors as compared with a physiological signal. Experiments were conducted in three different environmental scenarios. The scenarios were classified as 'urban,' 'highway,' and 'rural,' and they were chosen to simulate contrasting stress conditions experienced by the driver. In this paper, skin conductance and driver self-reports served as a reference stress to predict the driver's stress level. Galvanic skin response, a well-known stress indicator, was captured along the driver's palm and the readings were transmitted to a mobile device via low energy Bluetooth for further processing. The results revealed that indirect measurement of steering wheel movement with an inertial motion sensor could obtain accuracies up to an average rate of 94.78%. This demonstrates the opportunity for inclusion of motion sensors in wireless driver assistance systems for ambulatory monitoring of stress levels.

Original languageEnglish
Article number7725939
Pages (from-to)1835-1844
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Volume18
Issue number7
DOIs
Publication statusPublished - Jul 2017
Externally publishedYes

Keywords

  • Driver assistance
  • healthcare sensor
  • inertial motion unit
  • stress monitoring
  • wearable system

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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