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Estimation of food portion sizes through smartphone images (Ref: SF20/APP/BROWNLEE1)

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

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); or APEL evidence of substantial practitioner achievement.
• Appropriate IELTS score, if required.
• Applicants cannot apply for this funding if currently engaged in Doctoral study at Northumbria or elsewhere.

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: Applications should include a covering letter that includes a short summary (500 words max.) of a relevant piece of research that you have previously completed and the reasons you consider yourself suited to the project. Applications that do not include the advert reference (e.g. SF20/…) will not be considered.

Deadline for applications: 1st July for October start, or 1st December for March start
Start Date: October or March
Northumbria University takes pride in, and values, the quality and diversity of our staff. We welcome applications from all members of the community. The University holds an Athena SWAN Bronze award in recognition of our commitment to improving employment practices for the advancement of gender equality.

Please direct enquiries to Dr Iain Brownlee ()

Funding Notes

Please note, this is a self-funded project and does not include tuition fees or stipend; the studentship is available to Students Worldwide. Fee bands are available at View Website . A relevant fee band will be discussed at interview based on project running costs.

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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