Detection of driver drowsiness using wearable devices: A feasibility study of the proximity sensor
MetadataShow full item record
He, Jibo; Choi, William; Yang, Yan; Lu, Junshi; Wu, Xiaohui; Peng, Kaiping. 2017. Detection of driver drowsiness using wearable devices: A feasibility study of the proximity sensor. Applied Ergonomics, vol. 65:pp 473-480
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.
Click on the DOI link to access the article (may not be free).