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Personalized Smart Clothing Design Based on Multimodal Visual Data Detection.

Haijuan Deng1, Minglong Liu2

  • 1School of Art and Design,Suzhou University, Suzhou 234000, China.

Computational Intelligence and Neuroscience
|April 4, 2022
PubMed
Summary

This study introduces a new method for automatic clothing style generation and smart suit design, enabling user innovation and integrating sensing technology for outdoor sports apparel. The approach combines deep learning for image retrieval with genetic algorithms for style evolution and sensor integration for monitoring functions.

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Area of Science:

  • Computer Science
  • Fashion Design
  • Wearable Technology

Background:

  • Traditional clothing customization offers limited user design input and style variety.
  • Existing systems struggle to facilitate genuine user innovation in clothing design.
  • There's a growing need for smart clothing that integrates functionality with aesthetic appeal.

Purpose of the Study:

  • To develop an automatic clothing style generation method using parameterized coding and genetic algorithms.
  • To create an image retrieval model leveraging deep convolutional neural networks and multimodal correlation features.
  • To design outdoor sports smart clothing with integrated sensing technology for user monitoring.

Main Methods:

  • A novel multitask deep convolutional neural network for image retrieval using user click data.
  • Image-text multimodal correlation for calculating query-image and image-image relevance.
  • Genetic algorithm for clothing style evolution, with human-computer interaction for fitness assignment.
  • Parameterized coding and decoding for automatic generation of visual suit style diagrams.
  • Wearer-centered design principles and sensing technology (Arduino) for outdoor smart clothing.

Main Results:

  • Successful automatic generation of clothing styles and visual diagrams.
  • Development of an effective image retrieval model based on multimodal features.
  • Design of a smart suit prototype capable of monitoring heart rate and microclimate temperature.
  • Demonstration of integrating sensing devices with clothing for functional and aesthetic apparel.

Conclusions:

  • The proposed methods enable user-driven innovative clothing design and automatic style generation.
  • Smart clothing can be effectively designed by integrating sensing technology with aesthetic and ergonomic considerations.
  • This research provides a framework for future developments in personalized and functional smart apparel.