Research on Evaluation Model for Secondary Task Driving Safety Based on Driver Eye Movements

Jin, Lisheng; Xian, Huacai; Jiang, Yuying; Niu, Qingning; Xu, Meijiao; Yang, Dongmei

doi:10.1155/2014/624561

Cited by 11 publications

(4 citation statements)

References 13 publications

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“…The collected signals from the eight gestures were used for training and classification for robot manipulation. The EOG sensor mounted on a region of the forehead that was prepared by exfoliating the stratum corneum with tape yielding reproducible and high-quality results, as demonstrated in alpha rhythms recorded from awake subjects with their eyes closed [14,151,152]. The expected feature at 0~10 Hz could be seen in the Fourier-transformed data as shown in Fig.15(a) [153].…”

Section: Electrophysiology Sensorsmentioning

confidence: 99%

Soft human–machine interfaces: design, sensing and stimulation

Dong

Wang

Zhou

et al. 2018

Int J Intell Robot Appl

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Human-machine interfaces (HMIs) are widely studied to understand the human biomechanics and/or physiology and the interaction between humans and machines/robots. The conventional rigid or invasive HMIs that record/send information from/to human bodies have significant disadvantages in practice for longterm, portable, and comfortable usages. To better adapt to natural soft skins, soft HMIs have been designed to deform into arbitrary shapes, and their bendable, stretchable, compressible and twistable properties offer a huge potential in future personalized applications. This paper presents a survey on various soft HMIs in terms of design, sensing, stimulation as well as their applications. Specifically, tactile / motion / bio-potential sensors are categorized for recording various data from human bodies, while stimulators are discussed for information feedback and motion activation to human bodies. It is anticipated that soft HMIs will promote the interaction among humans, machines/robots and environment to achieve desired coexisting-cooperativecognitive function in a robot system, named as Tri-Co Robot, for the human-centered applications, such as rehabilitation, medical monitoring and human-robot cooperation.

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Section: Electrophysiology Sensorsmentioning

confidence: 99%

Soft human–machine interfaces: design, sensing and stimulation

Dong

Wang

Zhou

et al. 2018

Int J Intell Robot Appl

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“…Therefore, how to reduce traffic accidents would be studied in safety driving with sensing data analysis and fusion algorithms [8]. A variety of sensors, such as digital image sensors, accelerometers, and infrared sensors are developed to increase awareness and promote safety during the driving process [2,9,10]. Recently, epidermal electronics or soft electronic patches have been developed to monitor driving activities [7,11].…”

Section: Introductionmentioning

confidence: 99%

Soft Bimodal Sensor Array Based on Conductive Hydrogel for Driving Status Monitoring

Dong

Yao

Yang

2020

Sensors

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Driving status monitoring is important to safety driving which could be adopted to improve driving behaviors through hand gesture detection by wearable electronics. The soft bimodal sensor array (SBSA) composed of strain sensor array based on ionic conductive hydrogels and capacitive pressure sensor array based on ionic hydrogel electrodes is designed to monitor drivers’ hand gesture. SBSA is fabricated and assembled by the stretchable functional and structural materials through a sol–gel process for guaranteeing the overall softness of SBSA. The piezoresistive strain and capacitive pressure sensing abilities of SBSA are evaluated by the data acquisition system and signal analyzer with the external physical stimuli. The gauge factor (GF) of the strain sensor is 1.638 under stretched format, and –0.726 under compressed format; sensitivity of the pressure sensor is 0.267 kPa−1 below 3.45 and 0.0757 kPa−1 in the range of 3.45–12 kPa, which are sensitive enough to hand gesture detection and driving status monitoring. The simple recognition method for the driver’s status behavior is proposed to identify the driver’s behaviors with the piezoresistive properties of conductive polymers, and the turning angles are computed by the strain and pressure values from SBSA. This work demonstrates an effective approach to integrate SBSA seamlessly into an existing driving environment for driving status monitoring, expanding the applications of SBSA in wearable electronics.

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“…Young et al found that portable music player that features "touch technology" increased the amount of time that drivers spent with their eyes off the roadway and decreased their ability to maintain a constant lane position and time headway from a lead vehicle [6]. Jin et al reported that the invehicle touch-screen telephone weakened driver's ability to control the vehicle [7]. Victor et al research showed that the task difficulty also had significant effect on driver eye movements [8].…”

Section: Introductionmentioning

confidence: 99%

The Effects of Using In-Vehicle Computer on Driver Eye Movements and Driving Performance

Xian

Jin

2014

Advances in Mechanical Engineering

Self Cite

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This paper examined the effects of performing an e-mail receiving and sending task using in-vehicle computer (iPad4) on driving performance and driver eye movements to determine if performance decrements decreased with practice. Eighteen younger drivers completed the driving on driving simulator while interacting with or without an e-mail task. Measures of fixations, saccades, vehicle control, and completion time of the secondary task were analyzed. Results revealed that using in-vehicle computer featured "large touch-screen" to receive and send e-mail greatly weakened driver's distraction and decreased their ability to control the vehicle. There was also evidence that, however, drivers attempted to regulate their behavior when distracted by decreasing their driving speed and taking a large number of short fixations and a quick saccades towards the computer. The results suggest that performing e-mail receiving and sending tasks while driving is problematic and steps to prohibit this activity should be taken.

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Research on Evaluation Model for Secondary Task Driving Safety Based on Driver Eye Movements

Cited by 11 publications

References 13 publications

Soft human–machine interfaces: design, sensing and stimulation

Soft human–machine interfaces: design, sensing and stimulation

Soft Bimodal Sensor Array Based on Conductive Hydrogel for Driving Status Monitoring

The Effects of Using In-Vehicle Computer on Driver Eye Movements and Driving Performance

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