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Real Time Nutritional Intake Monitoring System

IP.com Disclosure Number: IPCOM000247119D
Publication Date: 2016-Aug-08
Document File: 3 page(s) / 47K

Publishing Venue

The IP.com Prior Art Database


A significant proportion of the population require monitoring of their daily nutritional intake, whether it is for medical reasons, such as diabetics, or simply wanting to lose weight. This currently means people have to look up nutritional information of the foods they eat and calculate totals for the nutritional values that are important to them. The current method of relying on the user finding these values and manually calculating totals is time consuming, this also makes it difficult for a user who doesn't know exactly what food they are eating such as when they are in a restaurant. This results in people calculating the value for the whole meal rather than just the food that they have eaten. Using image recognition it is possible to identify the foods that are on a plate and by using 3D image modeling it is possible to work out the volumes of these foods. Knowing the food type and volume, nutritional intake can then be calculated. This proposed method would use these technologies to periodically calculate throughout the meal nutritional information of the food eaten from the user's plate, feeding back this information in real time. This method is different to other solutions as it provides information on what the user has eaten rather than information about what is on their plate.

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Real Time Nutritional Intake Monitoring System

By 2025, it is estimated that 5 million people will have diabetes in the UK. The UK's diet industry is worth £2 billion, and the sports nutrition industry is worth £260 million. The problem identified is that there are many people who need to track a log of their nutritional intake, e.g. those with diabetes, kidney disease or those who want to lose weight. This is usually done through a manual tracking process whereby the user obtains the nutritional information by reading the packaging, asking around or estimating. This method is time consuming and difficult when eating at social events or out at a restaurant. Potential users include those with diabetes and kidney disease, athletes, and dieters. Users with kidney disease need to monitor their sodium, potassium, phosphorus, carbohydrate, protein and calorie intake. Users

with diabetes need to focus significantly on their carbohydrate intake. Athletes tend to monitor their protein, carbohydrate and daily fat intake. Users who undertake manual monitoring techniques often calculate their nutritional intake after they have consumed it.

    The idea here will take two photos from different angles to construct a 3D representation of the targeted meal. Image recognition can identify the different food items in the 3D representation and further technology will calculate the volume of said food items. The user can then begin eating and during this process, the app will continuously compare the newly created model to the last model to identify when food has left the model. The database will contain nutritional values e.g. per 100g, and the density. We calculate the mass of the food using the foods known density value, and the calculated foods calculated volume change, (Mass = Volume Change
* Density). Once we know the mass, we can identify the nutritional values and then

feed this back to the user, adding it to the sum of nutritional value for that current meal.

    The idea here works using similar technology to that of the Mobile Augmented Reality System for Portion Estimation referenced in the prior art in that both calculate the volume of the food and the nutritional value using an image taken by a smart device. Our solution improves upon this as through the use of smart glasses it can take images in real time and can calculate whilst the user is eating

what the nutritional value of the food they have eaten is rather than the nutritional

value of all the food on the plate. Ours can also take into account the nutritional value of the food added to the plate after the meal has started whereas the other system would require the user to take a second photograph of the extra food and calculate the added value themselves.

    The MANGO system is another system that uses images of the food to offer nutritional values for the intake of that food but once again it works on the assumption that the meal as a whole will be eaten.

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