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Appl Ergon. 2017 Nov;65:473-480. doi: 10.1016/j.apergo.2017.02.016. Epub 2017 Apr 15.

Detection of driver drowsiness using wearable devices: A feasibility study of the proximity sensor.

Author information

1
Laboratory of Emotion and Mental Health, Chongqing University of Arts and Sciences, Yongchuan, Chongqing, 402160, China; Department of Psychology, Wichita State University, Wichita, KS 67206, USA. Electronic address: jibo.he@wichita.edu.
2
Department of Psychology, Wichita State University, Wichita, KS 67206, USA.
3
Center of Intelligent Acoustics and Immersive Communications, and School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China. Electronic address: y.yang@nwpu.edu.cn.
4
School of Psychological and Cognitive Sciences, Peking University, Beijing, 100871, China.
5
Department of Psychology, Tsinghua University, Beijing, 100084, China.

Abstract

BACKGROUND:

Drowsiness is one of the major factors that cause crashes in the transportation industry. Drowsiness detection systems can alert drowsy operators and potentially reduce the risk of crashes. In this study, a Google-Glass-based drowsiness detection system was developed and validated.

METHODS:

The proximity sensor of Google Glass was used to monitor eye blink frequency. A simulated driving study was carried out to validate the system. Driving performance and eye blinks were compared between the two states of alertness and drowsiness while driving.

RESULTS:

Drowsy drivers increased frequency of eye blinks, produced longer braking response time and increased lane deviation, compared to when they were alert. A threshold algorithm for proximity sensor can reliably detect eye blinks and proved the feasibility of using Google Glass to detect operator drowsiness.

APPLICATIONS:

This technology provides a new platform to detect operator drowsiness and has the potential to reduce drowsiness-related crashes in driving and aviation.

KEYWORDS:

Driver drowsiness; Proximity sensor; Wearable device

PMID:
28420482
DOI:
10.1016/j.apergo.2017.02.016
[Indexed for MEDLINE]

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