I’m Isamu Sugita from Nara Institute of Science and Technology, Japan.
I’m here today to talk about A Method for Estimating Hunger Degree based on Meal and Exercise logs.
In this presentation, I’ll describe our approach for estimating hunger degree and experimental result.
Here you can see the outline of my presentation.
Here you can see the outline of my presentation.
Currently, increasing the number of obesity people is social problem.
Especially the WHO reported that about 1.4 billion people are suffering from obesity in all of the world.
Obesity is caused by disturbance of dietary life, for example, High caloric intake, Snacking a lot, and Nutritional imbalance could be picked up as the matter.
Those disturbance is caused by that people can’t recognize their hunger state clearly.
How do we approach to this problem?
We approach to this problem by estimating and visualizing hunger state of human.
Specifically, our work aims to Prevent obesity by recommending meals and exercises at appropriate times
and visualizing to make the user aware with a general mobile device.
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How will your work improve (recover) the obesity??
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I think, if our approach will be integrated to mobile healthcare-application or service,
existing system get ability for predicting user’s hunger state and supporting his or her dietary life.
Therefore, it will be possible to alert and recommend the better food and activity when user take much meal compared to regular days.
Here you can see the outline of my presentation.
First related work is Validated caloric expenditure estimation using a single body-worn sensor.
This work estimates caloric expenditure by measuring inspired and exhaled air and acceleration of multiple subjects during exercise which is prescribed from researchers.
I pick up the Trend estimation of blood glucose level fluctuation based on data mining as the related work
This work estimates morning of next day blood glucose level by building the estimation model based on the data collected from blood glucose monitor, metabolic rate monitor, and mobile computer from multiple subjects.
On the other hand, our work can estimate blood glucose and hunger degree in arbitrary time by using only mobile device,
and estimation target is healthy general person.
These are the difference between our work and related work.
Here you can see the outline of my presentation.
Before showing the estimation model, we must explain about association between hunger degree and blood glucose.
Blood glucose shows a concentration of glucose included in blood, and we use it as a ground truth of hunger degree in this work.
In the case of healthy person, from 70~109[mg/dL] when hunger, up to 140[mg/dL] after meal 90~120 minutes.
In the following figure, satiety feeling increase rapidly after taking meal, and decrease gradually.
Blood glucose has similar curve but also has about from 90 to 120 minutes delay time.
This figure shows organization of the hunger degree estimation model.
The hunger degree estimation model consists of two step estimation model.
At first, the estimated blood glucose is calculated from the meal and exercise logs by the first estimation model.
Secondly, the hunger degree is estimated from the blood glucose obtained from the first estimation model.
Finally, the hunger degree and blood glucose is visualized for user on a mobile device, and user can look his/her physical state.
The contents of meal and exercise logs is shown as following table.
Meal information consists of Time of taking meal, perceived quantity level of meal before taking, and feeling of fullness after taking.
Exercise information consists of the time when the new data was recorded, existence of sleeping hours after the previous record and the length of sleeping hours,
and caloric expenditure.
Meal information is a element increasing the blood glucose, and Exercise information is a element decreasing the blood glucose.
Those input parameter is input from smartphone application.
Above figure shows the blood glucose estimation model.
Coefficient of the estimation model is decided by fitting this formula to similar curve function.
We used the log-normal distribution function as similar curve (fitting) function, and decided each parameters based on the function.
Those parameters is decided in such a way that summation of square between training data and estimated value become minimum
This figure shows the hunger degree estimation model.
Hunger degree curve has negative correlation and about 90 minutes delay time for the blood glucose curve.
Therefore the estimation model formula considers the delay time Tg, and use the value which multiples maximal value by the parameter which 1 subtract normalized blood glucose.
Here you can see the outline of my presentation.
The purpose of this experiment is what investigates estimation accuracy of the estimation model and collects the dataset about training data.
As the procedure, subject conduct measurement of hunger degree and blood glucose with self-monitoring glucose kit, and record of meal and exercise logs 4 times at 2 hour intervals.
As experimental environment, subject is one 20’s adult male, experimental place is our laboratory, and Time period is 6-days during from June to July.
In the experiment, we use a self-monitoring glucose kit “Gluco card G Black” and other tools.
These are used for measuring blood glucose and sterilizing a puncture point.
Recorded information is meal and exercise logs as actual log information and hunger degree and measured blood glucose as ground-truth.
After recording those information, we compare between estimated value and training data and analyze.
This figure shows a comparison between measured and estimated blood glucose.
Solid line is measured value, and dashed line is estimated value.
For all days, Correlation value is 0.29, and estimation error is 14.39%.
As the result, our estimation model could estimated up-down variation of the blood glucose with a certain level of accuracy.
This figure shows a comparison between measured and estimated blood glucose.
Solid line is measured value, and dashed line is estimated value.
For all days, Correlation value is 0.29, and estimation error is 14.39%.
As the result, our estimation model could estimated up-down variation of the blood glucose with a certain level of accuracy.
This figure shows a comparison between measured and estimated hunger degree.
Correlation value is 0.77, and estimation error is 1.26 levels in 10 levels.
As the result, our estimation model could estimated transition of the hunger sensation with high accuracy.
This figure shows a comparison between measured and estimated hunger degree.
Correlation value is 0.77, and estimation error is 1.26 levels in 10 levels.
As the result, our estimation model could estimated transition of the hunger sensation with high accuracy.
Here you can see the outline of my presentation.
To simplify the input procedure, we developed an application for inputting meal and exercise logs and estimating blood glucose and hunger degree.
Those figures show the application user interface.
User can check the blood glucose and hunger degree in main information, and can input log information easily in each input form.
We proposed a method form estimating hunger degree and blood glucose based on the meal and exercise logs non-invasively.
And we found that our method can estimate hunger sensation and blood glucose in certain level of accuracy.
In the future plans, we’ll improve an estimation accuracy of blood glucose by considering contents or GI value of meal,
investigate availability of the estimation model by doing evaluation experiment of multiple subjects.