A novel method for the evaluation of fashion product design based on data mining

It is difficult to qualitatively evaluate the design effects of product appearance. Electroencephalograph (EEG) and eye-tracking data can serve as reflection of the subconscious activities of human beings. The application of advanced neuroscience technology in industrial operation management has bec...

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Bibliographic Details
Published in:Advances in manufacturing 2017-12, Vol.5 (4), p.370-376
Main Authors: Li, Bao-Rui, Wang, Yi, Wang, Ke-Sheng
Format: Article
Language:English
Subjects:
Brain
Control
Correlation analysis
Data mining
Design analysis
Electroencephalography
Engineering
Eye movements
Machines
Manufacturing
Mechatronics
Nanotechnology and Microengineering
Outsourcing
Processes
Product design
Robotics
Tracking devices
Uniqueness
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Summary:It is difficult to qualitatively evaluate the design effects of product appearance. Electroencephalograph (EEG) and eye-tracking data can serve as reflection of the subconscious activities of human beings. The application of advanced neuroscience technology in industrial operation management has become a new research hot spot. This study uses EEG equipment and an eye-tracking device to record a subject’s brain activity and eye-gaze data, and then uses data mining methods to analyze the correlation between the two types of signals. The fuzzy theory is then applied to create a fuzzy comprehensive evaluation model. The neural attributes are used to quantify the factors affected by product appearance and evaluation indicators. We use women’s shirts as research subjects for a case study. The EEG Emotiv device and Tobii mobile eye-tracking glasses are used to record a subject’s brain activity and eye-gaze data in order to quantify the evaluation factors related to product appearance. This method not only scientifically evaluates the uniqueness of product appearance but also provides an objective reference for improving product appearance design.
ISSN:2095-3127
2195-3597
DOI:10.1007/s40436-017-0201-x