Opportunities for using eye tracking technology in manufacturing and logistics: Systematic literature review and research agenda

Tian, Zheng; Glock, C. H.; Grosse, E. H.

doi:10.1016/j.cie.2022.108444

Cited by 16 publications

(3 citation statements)

References 88 publications

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“…This method allowed AI systems to predict driver situational awareness in relation to levels 1 and 2 of the Endsley model (perception and comprehension). Reference [45], in their literature review, additionally highlighted that pupil measurements, specifically focusing on pupil diameter, may serve as an indication of the user's emotional arousal.…”

Section: Eye-tracking Metrics and Oculomotor Eventsmentioning

confidence: 99%

Situational Awareness Assessment of Drivers Boosted by Eye-Tracking Metrics: A Literature Review

Arias-Portela,

Mora-Vargas,

Caro

2024

Applied Sciences

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The conceptual framework for assessing the situational awareness (SA) of drivers consists of three hierarchical levels: perception of the elements of the environment, comprehension of the elements, and decision-making in the near future. A common challenge in evaluating SA is the determination of the available subjective and objective techniques and their selection and integration into methodologies. Among the objective techniques, eye tracking is commonly used, considering the influence of gaze behavior on driving. This review is presented as an innovative approach to the subject matter, introducing physiological metrics based on eye tracking and investigating their application in assessing the SA of drivers. In addition, experiments and methodologies that revealed patterns at the three levels of SA were identified. For this purpose, databases were searched, and 38 papers were considered. Articles were clustered according to prevalent themes such as eye-tracking metrics, eye-tracking devices, experiment design, and the relationship between SA and eye-tracking. This review summarizes the main metrics and key findings for each article and reveals a wide relationship between the eye-tracking metrics and SA. The influence of appropriately calibrated equipment, refined data collection protocols, and adequate selection of the eye-tracking metrics was examined. Further reviews are needed to systematically collect more evidence.

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Section: Eye-tracking Metrics and Oculomotor Eventsmentioning

confidence: 99%

Situational Awareness Assessment of Drivers Boosted by Eye-Tracking Metrics: A Literature Review

Arias-Portela,

Mora-Vargas,

Caro

2024

Applied Sciences

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“…Indeed, Lu et al [48] gauged the importance of selecting the most appropriate sensorial communication mode (spoken, gaze, gestures) depending on the level of human-machine comprehension preferred. Such potentials encompass (but are not restricted to): tilting of the head and neck [68]; gaze and eye-tracking [69]; speech [62]; and gestures [2] to retain autonomy of the system by a person with disabilities. Lu et al [48] deem gestures and action detection as the most applicable for human goal and action understanding by the system.…”

Section: Projectors and Thermography Technologymentioning

confidence: 99%

Smart and Sustainable Human-Centred Workstations for Operators with Disability in the Age of Industry 5.0: A Systematic Review

Bonello,

Francalanza,

Refalo

2023

Sustainability

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The World Bank has reported that over one billion individuals have a disability, implying that almost fifteen percent of the global inhabitants are susceptible to undergoing levels of discrimination, especially in employment. This issue may prevail on a manufacturing shop floor, whereby a wave of standardisation dominates such as in the design of shop floor workstations. Despite advances made in the literature, people with disabilities are still siloed from manufacturing. Consequently, the aim of this research work was to analyse literature’s current state of the art on the design of workstations for operators with disabilities within the context of Industry 5.0, where sustainability, human-centricity, and resilience are upheld. The study employed a systematic review of 69 publications from Scopus and Google Scholar published between 2013 and 2023, adhering to the updated PRISMA guidelines to identify the major research gaps. The review contributes an understanding of the current academic and industrial limitations such as the absence of social applicability of Industry 4.0 technology, the rift between academic knowhow and industrial implementation, and the lack of alignment with the sustainable development goals (SDGs). Additionally, the review uncovered an absence in work bridging four disciplines together: workstation design, Industry 5.0, sustainability, and disability. An unprecedented understanding of the interdependency between all four disciplines within the remit of smart, sustainable, and inclusive manufacturing workstations is contributed. This review proposes directions amidst the four most relevant SDGs—SDGs 8, 9, 10, and 12 to the topic.

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“…2. Head Movement Detection, method utilizes a camera to observe the driver's head movement (Zheng et al, 2022). If drivers experience fatigue or drowsiness, their head movements will become unstable or often experience a significant reduction.…”

Section: Introductionmentioning

confidence: 99%

Drowsy Detection in the Eye Area using the Convolutional Neural Network

Wedha

Rahman

Hindarto

et al. 2023

SinkrOn

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Detection of a drowsy driver is an important aspect of driving safety. For this reason, it is necessary to have technology to carry out early detection before fatigue occurs. Mainly focused on driver fatigue that occurs at night. Analysis can be done quickly and accurately. These conditions can be sent via data so that they can be monitored and analyzed in real time. The results of the analysis can be sent by communication via the internet network. In addition, it functions as an early warning and can be used as logging or records that can be stored. This research does not discuss data communication but makes a prototype for detecting sleepy drivers. Prototype created using the Convolutional Neural Network Algorithm. The detection area is in the eye and testing is carried out with the brightness level of the light. In this study, building a prototype to detect signs of driver fatigue using the Convolutional Neural Network algorithm. The detection area used is in the eye, by testing at different light brightness levels. The dataset used in this study consists of a series of eye images, which are divided into two classes, namely open eyes, and closed eyes. After conducting the training process on Convolutional Neural Network, we get results of detection accuracy reaching 90%.

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Opportunities for using eye tracking technology in manufacturing and logistics: Systematic literature review and research agenda

Cited by 16 publications

References 88 publications

Situational Awareness Assessment of Drivers Boosted by Eye-Tracking Metrics: A Literature Review

Situational Awareness Assessment of Drivers Boosted by Eye-Tracking Metrics: A Literature Review

Smart and Sustainable Human-Centred Workstations for Operators with Disability in the Age of Industry 5.0: A Systematic Review

Drowsy Detection in the Eye Area using the Convolutional Neural Network

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