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DDRS – Dietary Data Recording System

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Presentation on theme: "DDRS – Dietary Data Recording System"— Presentation transcript:

1 DDRS – Dietary Data Recording System
The Dietary Data Recording System supports the dietary assessment of cancer patient participants by facilitating collection of more objective measurement of dietary intake in real time and with moderate cost.

2 Motivation Relationship between Diet and Health
Increase study participants satisfaction! Inaccuracies from under reporting intake Cost Reduction (Doubly Labeled Water) Address issues with traditional dietary recording systems Inconvenience of remembering food log

3 DDRS Dataflow Solution components nutritionist, device use,
data management Study participants log food intake on a device. Data uploaded to a server which handles image processing and volume estimate. The collected and calculated values are imported into a web interface for review by a clinical coder. The Coders prepare the data for entry into NDSR which can be used for performing nutrient analysis based on the data.

4 Data Flow Meal Entries Via Android App Segmentation Volume Estimation
Data Publication Coder Interface NDSR - Nutrition Analysis

5 Data Collection

6 DDRS APP

7 Meal Entries

8 DDRS - Food Segmentation

9 Volumetric Analysis

10 Coder Interface

11 Other Techniques of Segmentation and Volume Estimation
Brightness down by -100 Contrast up by 70 Grayscale

12 raw image blurred image
To solve this problem we make two kinds of operations ahead of Canny operation, which are Smooth and Erode. Both of them are used to blur the image to remove tiny noise dots. In the right image, we can see the plate edge is almost perfect and this bright noise is merged into its neighbors and its edge is vague. raw image blurred image

13 Canny on the blurred image
Now we can implement Canny. The result image has just two smooth contours that we care about without noise or any other contour we don’t need. The contours can be clearly seen by our eyes. blurred image Canny on the blurred image

14 But it’s not enough just showing an image with contours
But it’s not enough just showing an image with contours. The ultimate goal is to make the computer recognize the objects. So right now we need to mark the contours and store them in the memory by some data structure. In OpenCV there is a library function called cvFindContours(), which is able to detect all the closed contours in an image and store them as a linked list. Here is the result. It was the Canny operation that makes this function able to find contours very accurately without any trivial noise. cvFindContours()

15 3D Point Cloud We will use a cellphone with a laser on it. Unlike usual laser devices, this laser will emit a line onto a surface. We hold this line to scan through the food on a plate from one side to the other.

16 Front View of an example cloud
3D Point Cloud Then we can get a set of data visualized like this. This is the front view of the point cloud, because each line is made from points and all the lines form the cloud shaped data. We will use this data to estimate the volume of the thing it covers. Front View of an example cloud

17 Top View of another example
3D Point Cloud This is the top view of another example cloud data. The black part is the plate holding the food, and the blue part is the food. At last we use the segmentation image to cancel the plate part in the image so that we have only the food point cloud. Then we can use some math model to calculate the food volume. Top View of another example

18 Intelligent Trash Can for Smart Home/Restaurants
Data Collection Data Processing Data Analytics Applications Kinects Cheap cameras Weighing scale Better Waste Management Cost Reduction Greener planet Image detection Volumetric Analysis Carbon footprint measurement Demographic waste information

19 Call for Papers - LinuxFest
What – LinuxFest Northwest 2013 When – April ’13 Where – Bellingham Technical College, WA Presentation proposals due – 15 March ’13

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