Abstract
The recent development of smart home technology is making people’s residential life more affluent. Numerous advanced technologies are being applied to various home appliances and furniture including smart boiler, smart refrigerator and smart bed changing people’s everyday life gradually. The shoe cabinet is the furniture people pass through first when entering a house, and it was thought that if IoT function is attached to a shoe cabinet it will give much convenience to people as a component of the smart home just like smart boiler and smart refrigerator. On such a thought and trend, a small processor like Raspberry Pi was attached to the shoe cabinet at home to see the list of shoes, store shoes automatically and recommend right shoes for occasions. The shoes that the user wants to put into the shoe cabinet are stored automatically in the empty space of the shoe cabinet using x-y floater, and the shoe cabinet stores shoes by dividing the kinds and colors of shoes. And it is possible to see the status of the shoes in the shoe cabinet remotely using a mobile application. Moreover, the most appropriate shoes will be recommended when information on type of clothes worn and the destination are put in. The automatic storage of shoes was realized by controlling the input sensor and x-y floater with the Raspberry Pi attached to the shoe cabinet. Classification of shoe images was realized by putting in already classified shoe image data called ‘UT Zappos50K’ in the Deep Learning model made with Keras framework. Shoe recommendation service is made possible by entering the info on condition of user’s clothes and destination and recommending the shoes which score the highest among the shoes in the shoe cabinet using the prepared score chart.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Huh J-H, Otgonchimeg S, Seo K (2016) Advanced metering infrastructure design and test bed experiment using intelligent agents: focusing on the PLC network base technology for Smart Grid system. J Supercomput 72(5):1862–1877
Huh J-H (2017) PLC-based design of monitoring system for ICT-integrated vertical fish farm. Human-centric Comput Inf Sci 7(20):1–19
Vosoughi Soroush, Roy Deb, Aral Sinan (2018) The spread of true and false news online. Sci AAAS 359(6380):1146–1151
Jordan MI, Mitchell TM (2015) Machine learning: Trends, perspectives, and prospects. Sci AAAS 263(349):255–260
Hastie T, Tibshirani R, Friedman J (2008) The elements of statistical learning; data mining, inference and prediction
Murphy KP (2012) Machine learning: a probabilistic perspective. MIT press, Cambridge
Doan, T.; Kalita, J.; “Predicting run time of classification algorithms using meta-learning,” International Journal of Machine Learning and Cybernetics, Springer, 2016, 1–15
Huh J-H, Kim T-J (2018) A location-based mobile health care facility search system for senior citizens. J Supercomput 1–18
Garcia Lopez P et al (2015) Edge-centric computing: vision and challenges. SIGCOMM Comput Commun Rev 45:37–42
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Huh, JH., Seo, K. (2019). Artificial Intelligence Shoe Cabinet Using Deep Learning for Smart Home. In: Park, J., Loia, V., Choo, KK., Yi, G. (eds) Advanced Multimedia and Ubiquitous Engineering. MUE FutureTech 2018 2018. Lecture Notes in Electrical Engineering, vol 518. Springer, Singapore. https://doi.org/10.1007/978-981-13-1328-8_108
Download citation
DOI: https://doi.org/10.1007/978-981-13-1328-8_108
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1327-1
Online ISBN: 978-981-13-1328-8
eBook Packages: EngineeringEngineering (R0)