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  Estimation of food portion sizes through smartphone images (ref: SF22/HLS/APP/BROWNLEE)


   Faculty of Health and Life Sciences

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  Dr I Brownlee  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Accurate and precise assessment of dietary intake is crucial to consideration of population-to individual-level patterns of food consumption with clear links to consequent impacts on long-term health and national/global food requirements. A major source of inaccuracy in estimating dietary intake occurs when gauging food portion sizes and portion sizes can be highly variable. As long-term dietary patterns of overconsumption have negative consequence on long-term health (e.g. increased risk of obesity, type II diabetes, cardiovascular disease, cancer risk), there is a need for more practical solutions for estimating food portion size.

Currently, the most commonly-used “gold standard” for dietary assessment is weighed food diaries. This approach tends to have high-participant burden which can greatly reduce compliance and even alter an individual’s eating habits. More user-friendly methods to assess dietary intake will benefit multiple stakeholders, including individual consumers (to aid body weight management) to vendors (to limit food waste and maximise profits) and public health agencies (to reduce the incidence of major age-related disease). More objective dietary data will also greatly benefit many areas of human nutrition research.

The proposed studentship will aim to:

·        Define relevant foods to model with variable portion sizes based on national dietary intake data.

·        Develop image analysis algorithms to estimate food portion size from food images, for subsequent use in a mobile phone application.

Eligibility and How to Apply:

Please note eligibility requirement:

•      Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non- UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above)

•      Appropriate IELTS score, if required

For further details of how to apply, entry requirements and the application form, see https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/

 

Please note: All applications must include a covering letter (up to 1000 words maximum) including why you are interested in this PhD, a summary of the relevant experience you can bring to this project and of your understanding of this subject area with relevant references (beyond the information already provided in the advert). Applications that do not include the advert reference (e.g. SF22/…) will not be considered.

 

Deadline for applications: Ongoing

Start Date: 1st October and 1st March are the standard cohort start dates each year.

Northumbria University is committed to creating an inclusive culture where we take pride in, and value, the diversity of our doctoral students. We encourage and welcome applications from all members of the community. The University hold a bronze Athena Swan award in recognition of our commitment to advancing gender equality, we are a Disability Confident Employer, a member of the Race Equality Charter and are participating in the Stonewall Diversity Champion Programme. We also hold the HR Excellence in Research award for implementing the concordat supporting the career development of researchers.

Informal enquiries to Dr Iain Brownlee ([Email Address Removed])

Computer Science (8) Food Sciences (15) Mathematics (25)

Funding Notes

This project is fully self-funded and available to applicants worldwide. Tuition fees will depend on the running cost of the individual project, in line with University fee bands found at https://www.northumbria.ac.uk/study-at-northumbria/fees-funding/. The fee will be discussed and agreed at interview stage.
Please note: to be classed as a Home student, candidates must meet the following criteria:
Be a UK National (meeting residency requirements), or
have settled status, or
have pre-settled status (meeting residency requirements), or
have indefinite leave to remain or enter.
If a candidate does not meet the criteria above, they would be classed as an International student.

References

Brownlee, I.A., Low, J., Duriraju, N., Chun, M., Ong, J.X.Y., Tay, M.E., Hendrie, G.A. and Santos-Merx, L., 2019. Evaluation of the Proximity of Singaporean Children’s Dietary Habits to Food-Based Dietary Guidelines. Nutrients 11, 2615.
Neo, J.E., Salleh, S.B.M., Toh, Y.X., How, K.Y.L., Tee, M., Mann, K., Hopkins, S., Thielecke, F., Seal, C.J. and Brownlee, I.A., 2016. Whole-grain food consumption in Singaporean children aged 6–12 years. Journal of Nutritional Science, 5, 25.
Sulistyo, S.B., Wu, D., Woo, W.L., Dlay, S.S., and Gao, B., 2018. Computational Deep Intelligence Vision Sensing for Nutrient Content Estimation in Agricultural Automation. IEEE Transactions on Automation Science and Engineering, 15, 1243-1257.
Sulistyo, S.B., Woo, W.L., Dlay, S.S., and Gao, B., 2018. Building A Globally Optimized Computational Intelligence Image Processing Algorithm for On-Site Nitrogen Status Analysis in Plants. IEEE Intelligent Systems, 33, 15-26.

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