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Using Deep Learning in Real-Time for Clothing Classification with Connected Thermostats

Author

Listed:
  • Adán Medina

    (School of Engineering and Sciences, Tecnologico de Monterrey, Mexico City 14380, Mexico)

  • Juana Isabel Méndez

    (School of Engineering and Sciences, Tecnologico de Monterrey, Mexico City 14380, Mexico)

  • Pedro Ponce

    (School of Engineering and Sciences, Tecnologico de Monterrey, Mexico City 14380, Mexico)

  • Therese Peffer

    (Institute for Energy and Environment, University of California, Berkeley, CA 94720, USA)

  • Alan Meier

    (Energy and Efficiency Institute, University of California, Davis, CA 95616, USA)

  • Arturo Molina

    (School of Engineering and Sciences, Tecnologico de Monterrey, Mexico City 14380, Mexico)

Abstract

Thermal comfort is associated with clothing insulation, conveying a level of satisfaction with the thermal surroundings. Besides, clothing insulation is commonly associated with indoor thermal comfort. However, clothing classification in smart homes might save energy when the end-user wears appropriate clothes to save energy and obtain thermal comfort. Furthermore, object detection and classification through Convolutional Neural Networks has increased over the last decade. There are real-time clothing garment classifiers, but these are oriented towards single garment recognition for texture, fabric, shape, or style. Consequently, this paper proposes a CNN model classification for the implementation of these classifiers on cameras. First, the Fashion MNIST was analyzed and compared with the VGG16, Inceptionvv4, TinyYOLOv3, and ResNet18 classification algorithms to determine the best clo classifier. Then, for real-time analysis, a new dataset with 12,000 images was created and analyzed with the YOLOv3 and TinyYOLO. Finally, an Azure Kinect DT was employed to analyze the clo value in real-time. Moreover, real-time analysis can be employed with any other webcam. The model recognizes at least three garments of a clothing ensemble, proving that it identifies more than a single clothing garment. Besides, the model has at least 90% accuracy in the test dataset, ensuring that it can be generalized and is not overfitting.

Suggested Citation

  • Adán Medina & Juana Isabel Méndez & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2022. "Using Deep Learning in Real-Time for Clothing Classification with Connected Thermostats," Energies, MDPI, vol. 15(5), pages 1-28, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1811-:d:761611
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    References listed on IDEAS

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    1. Hilde Solli & Sigrun Hvalvik & Ida Torunn Bjørk & Ragnhild Hellesø, 2015. "Characteristics of the relationship that develops from nurse‐caregiver communication during telecare," Journal of Clinical Nursing, John Wiley & Sons, vol. 24(13-14), pages 1995-2004, July.
    2. Manuel Avila & Juana Isabel Méndez & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2021. "Energy Management System Based on a Gamified Application for Households," Energies, MDPI, vol. 14(12), pages 1-27, June.
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    Cited by:

    1. Juana Isabel Méndez & Adán Medina & Pedro Ponce & Therese Peffer & Alan Meier & Arturo Molina, 2022. "Evolving Gamified Smart Communities in Mexico to Save Energy in Communities through Intelligent Interfaces," Energies, MDPI, vol. 15(15), pages 1-29, July.

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