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Use of smartphones to estimate carbohydrates in foods for diabetes managementJurong HUANG, Hang DING, Simon MCBRIDE, David IRELAND, Mohan KARUNANITHI
HEALTH AND BIOSECURITY
Presenter: Hang Ding | [email protected] | HIC 2015
3 August 2015
Prevalence of diabetes
Use of smartphones to estimate carbohydrates in foods for diabetes management | Hang Ding, 3 August 20152 |
1.5 million Deaths directly caused by diabetes
380 million Adults with diabetes worldwide
Traditional estimation of carbohydrate
Use of smartphones to estimate carbohydrates in foods for diabetes management | Hang Ding, 3 August 20153 |
Smartphone Approach
Use of smartphones to estimate carbohydrates in foods for diabetes management | Hang Ding, 3 August 20154 |
OpenCV
Camera
OS
Food
Classifier
Nutrition
Database
Volume
Estimator
Carbohydrate
Calculation
Food Classifier
Use of smartphones to estimate carbohydrates in foods for diabetes management | Hang Ding, 3 August 20155 |
Three Features
Colour(RGB elements)
Shape(scale Invariant Feature Transform)
Texture(Local Binary Pattern)
Support Vector Machine
Volume Estimator
Use of smartphones to estimate carbohydrates in foods for diabetes management | Hang Ding, 3 August 20156 |
Food Photo
Object with calibrated size
Objects extracted Estimated Volume
Evaluation of Classification
Use of smartphones to estimate carbohydrates in foods for diabetes management | Hang Ding, 3 August 20157 |
10 types of fruits, 60 photos each(orange, apple, pear, tomato, strawberry, banana, mango,
avocado, pineapple, and kiwi fruit)
Training Data
10 types, 50 photos each
Test Data
10 types, 10 photos each
Randomized
ACC =
(TP + TN)
TP + TN + FP + FNOptimized Classification
Parameters
Accuracy of
Classification
Accuracy of Classification
Use of smartphones to estimate carbohydrates in foods for diabetes management | Hang Ding, 3 August 20158 |
Types of Tested Fruits
Cla
ssif
icat
ion
Acc
ura
cy
Accuracy of Carbo estimation
Use of smartphones to estimate carbohydrates in foods for diabetes management | Hang Ding, 3 August 20159 |
Test Item Model Volume
(ml)
Actual Volume
(ml)
Error Rate (%)
Estimated Carbs (g)
Actual Carbs (g)
Error Rate (%)
Peach 158 151 4.43 16.3 15.9 2.45
Apple1 165 173 4.85 18.5 21.3 15.1
Apple2 172 190 13.9 19.3 22.4 16.1
Apple3 201 198 1.49 22.5 23.7 3.56
Tomato1 21 22 4.76 0.74 0.78 5.41
Tomato2 17 19 11.7 0.62 0.66 6.45
Average Error 6.86 8.18
Table 1. Summary of the volume and carbohydrate estimations, compared with the
actual values measured from the water displacement and weight scale.
Future work
Use of smartphones to estimate carbohydrates in foods for diabetes management | Hang Ding, 3 August 201510 |
•Improvement of the approach
•Combination with other techniques
•Evaluation through clinical studies
Contact Us
Phone: 1300 363 400 or +61 3 9545 2176
Email: [email protected] Web: www.csiro.au
Thank you
Dr. David HansenDr. Mohan KarunanithiDr. Simon McBrideDr. David IrelandDr. Farhad Fatehi
Prof. Len GrayProf. Anthony RussellMs. Denise BennettsMs. Dominique Bird
Mr. Jurong Huang
