Abstract
The apparel e-commerce industry is growing day by day. In recent times, consumers are particularly interested in an easy and time-saving way of online apparel shopping. In addition, the COVID-19 pandemic has generated more need for an effective and convenient online shopping solution for consumers. However, online shopping, particularly online apparel shopping, has several challenges for consumers. These issues include sizing, fit, return, and cost concerns. Especially, the fit issue is one of the cardinal factors causing hesitance and drawback in online apparel purchases. The conventional method of clothing fit detection based on body shapes relies upon manual body measurements. Since no convenient and easy-to-use method has been proposed for body shape detection, we propose an interactive smartphone application, “SmartFit”, that will provide the optimal fitting clothing recommendation to the consumer by detecting their body shape. This optimal recommendation is provided by using image processing and machine learning that are solely dependent on smartphone images. Our preliminary assessment of the developed model shows an accuracy of 87.50% for body shape detection, producing a promising solution to the fit detection problem persisting in the digital apparel market.
Highlights
Societal demands for purchasing apparel products online have increased with technological progress
In our proposed method, k-Nearest Neighborhood (k-NN) or Convolutional Neural Network (CNN) is adopted for a body shape detection algorithm due to their relatively high accuracy performance. k-NN is a representative of a group of machine learning classifiers which is widely used in classifications in various fields including handwritten digit recognition from images [45] and tumor classification from images [46] and simple in procedure, calculating the Euclidian distance between da
K-NN or CNN is adopted for a body shape detection algorithm due to their relatively high accuracy performance. k-NN is a representative of a group of machine learning classifiers which is widely used in classifications in various fields including handwritten digit recognition from images [45] and tumor classification from images [46] and simple in procedure, calculating the Euclidian distance between dataset input images [47]
Summary
Societal demands for purchasing apparel products online have increased with technological progress. Tech startups like True Fit (www.truefit.com) and Fits.me (www.fits.me) have developed virtual fitting rooms using virtual mannequins to mimic the body measurements of shoppers and display what a certain garment, i.e., apparel, might look like when it is worn by the consumer [6]. These types of solutions fail in the practical size assessment at the industrial level due to technical barriers such as an overcomplicated process, inefficient technology, low user adaption and costly expenses required to adopt the technology [7]. Even advanced technologies such as Sizer (www.sizer.me) and MTailor (www.mtailor.com) do not always provide actual fit information and size measurements, failing to completely solve the poor fit issue [8]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
