Standalone Wearable Driver Drowsiness Detection System in a Smartwatch

Boon Leng Lee, Boon Giin Lee, Wan Young Chung

Research output: Journal PublicationArticlepeer-review

57 Citations (Scopus)


Drowsiness while driving is one of the main causes of fatal accidents, especially on monotonous routes such as highways. The goal of this paper is to design a completely standalone, distraction-free, and wearable system for driver drowsiness detection by incorporating the system in a smartwatch. The main objective is to detect the driver's drowsiness level based on the driver behavior derived from the motion data collected from the built-in motion sensors in the smartwatch, such as the accelerometer and the gyroscope. For this purpose, the magnitudes of hand movements are extracted from the motion data and are used to calculate the time, spectral, and phase domain features. The features are selected based on the feature correlation method. Eight features serve as an input to a support vector machine (SVM) classifier. After the SVM training and testing, the highest obtained accuracy was 98.15% (Karolinska sleepiness scale). This user-predefined system can be used by both left-handed and right-handed users, because different SVM models are used for different hands. This is an effective, safe, and distraction-free system for the detection of driver drowsiness.

Original languageEnglish
Article number7484220
Pages (from-to)5444-5451
Number of pages8
JournalIEEE Sensors Journal
Issue number13
Publication statusPublished - 1 Jul 2016
Externally publishedYes


  • Driver drowsiness detection
  • motion sensors
  • smartwatch
  • wearable

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering


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