Kapitel/Beitrag aus dem Buch: Beyerer J. & Längle T. 2021. OCM 2021 – 5th International Conference on Optical Characterization of Materials, March 17th – 18th, 2021, Karlsruhe, Germany : Conference Proceedings.
Personal(ised) sensors or ”wearables” could, in the future,
be applied to provide personalized nutritional advice. A
challenge here is to assess dietary intake. To date, this has mainly
been done with questionnaires or interviews, for example, food
frequency questionnaires or 24-hour recalls. However, these
methods are prone to bias due to conscious or unconscious misreports,
and more objective measurement methods are desirable.
By applying and combining spectral imaging techniques like hyperspectral
imaging (HSI) and RGB-depth (RGBD) imaging, information
on the macro-composition, identity and quantity of
food consumed can be obtained. In this work, we demonstrate
that HSI was effective for estimation of the fat content and layer
thickness of butter on slices of bread with root mean squared
errors of predictions of 4.6 (fat w/w %) and 0.056 mm respectively.
Identification and volume estimation of vegetables and
preparation methods were successful with RGBD imaging. Using
Convolutional Neural Networks, all samples were correctly
identified. For volume estimations of vegetables, R-square scores
between 0.80 – 0.96 were achieved.
Weesepoel, Y et al. 2021. Towards the universal assessment of dietaryintake using spectral imaging solutions. In: Beyerer J. & Längle T (eds.), OCM 2021 – 5th International Conference on Optical Characterization of Materials, March 17th – 18th, 2021, Karlsruhe, Germany : Conference Proceedings. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.58895/ksp/1000128686-4
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Veröffentlicht am 17. März 2021