Many of us are familiar with the variation of the fit of high street clothing, however, we rarely see the uniformity of shape expectations embedded in sizing practices in among this variation. Population data is increasingly easy to collect, using image capture technology/body scanning. Whilst our ability to collect population data increases, how we seek to accommodate populations with clothing offerings is extremely limited. The focus of this project is to explore the use of statistical analysis and artificial intelligence (AI) to (i) gain a better understanding of the relationship between the sizing of high street clothing available to the population and what the population actually needs captured in the form of 3-D Body Scans and (ii) support decision making of high street retailers when it comes to clothing sizing offerings.
The starting point of the research are 3-D Body Scans (more than 1000 scans are available) acquired over the past six years at UoM and a database containing commercially available clothing sizes of many high street retailers. Analyzing information extracted from body scans and available sizing offerings can help, for example, identify gaps in the market, explore alternative models of sizing, and study the evolution of the market and population needs and/or over/undersaturation of market segments (using for example clustering analysis). In turn, this information can be used to improve decision making of high street retailers, e.g. the use of (multi-objective) optimization can help retailers in determining which sizes to offer to meet business goals while being inclusive, and novel visualisation tools may find application in presenting better clothing and sizing information to our populations and retailers.
With a supervisory team covering expertise in apparel engineering, sizing, and AI, there is sufficient freedom in this project for the candidate to explore additional datasets, propose novel research questions and applications of the data, and develop and apply novel AI methods to improve garment fit and sizing communication.
Academic background of candidates
A good first degree or Masters in subjects such as computing, engineering, statistics, data science.
At the University of Manchester, we pride ourselves on our commitment to fairness, inclusion and respect in everything we do. We welcome applications from people of all backgrounds and identities and encourage you to bring your whole self to work and study. We will ensure that your application is given full consideration without regard to your race, religion, gender, gender identity or expression, sexual orientation, nationality, disability, age, marital or pregnancy status, or socioeconomic background. All PhD places will be awarded on the basis of merit.
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