Use of smartphones to estimate carbohydrates in foods for ... · Evaluation of Classification 7 |...

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 | hang.ding@csiro.au | 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

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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: enquiries@csiro.au 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