Solution #1: Vibration Ring

This design has two main parts. The first part is the ring to wear on hand. The second part is the application on the smartphone. For the first part, the ring has the sensor to keep track of the motion of the hand movement when eating. The sensor part is used to identify the speed that the hand moves when eating in order to notify the user. In addition, the ring will notify the user by vibrating whenever they can take the next bite. For the application on the phone, the software will receive the signal from the ring (i.e. get data from the ring) and analyze it.

Solution #2: Gastric Sensor

The acid sensor would use real-time data analysis in order to alert the user that they feel “full” before the satiety process even alerts the brain that they feel this way. The enteroendocrine cells of the GI tract function as a luminal surveillance system, which senses and respond to the appearance of food in the gut lumen. The products from decretion process help to regulate the digestion and detect the delivery of nutrients to the stomach by managing the food intake.

Solution #3: Timer and Analysis Application

The solution for this design is to keep track of the time of people when eating in order to notify them about the time. The brain needs about 15 minutes to receive the signal that it is full of the stomach, so they need to spend more time to chew per bite in a meal. For example, the timer will start counting down about 30 seconds for each bite and send the vibration signal to the phone, and then it restarts again (or the user will restart it). In order to control eating behavior, the application will be implemented the notification, such as vibration or sound, to let the users know. Besides, the application will also have the data analysis to keep track with the BMI and calorie intake per meal of user.

• The proposed design solution is to implement the timer to the iPhone application to control eating habit by using the two main parts: the timer with iterations for every 30 seconds by vibration or sound to notify the users to eat slower, and the statistical page to store and keep track with the performance of users for better improvement.
• The strengths of this design is really cost-effective and fast response for the users. Therefore, users are able to use the application every time they want to use it. However, the weakness of this design is the correlation between eating speed and calorie intake is not actually proven, although there are many researches shown about it but the relationship is not strongly supported.

Design Requirements and Specifications

• The countdown timer application with notification
• The database to keep track with the BMI and calorie intake per meal of user
  • Use FireBase database to store the data
• The options for user to adjust
  • Time per bite
  • Notification

Discussion of testing time results:

As can be seen from the chart, the most amount of people spend 15-30 minutes overall per meal. These iterations dominate the results from lunch and dinner. However, in the breakfast, snack, and other meal categories, a majority of people responded with less than 5 minutes or 5-15 minutes. This means that the majority of people are rushing through their meals during breakfast and various snacks. Since the device is designed to help users slow down their eating times, the device would appear to be most effective when used during the meals of breakfast and snacks.

Testing the Application on Potential Users

The application was tested on potential users to see how it would affect their eating times. In the study, two potential users were asked to eat a 770 calorie meal with a 30 second time interval between each bite. At another time, two people were asked to eat a 770 calorie meal without any disruption. The only other thing which the subjects were asked to do was record the number of bites which they took.

Discussion of the results

There is also a lot of variance in the data since there were not a large amount of test subjects. The data could be skewed if the non-adhering subjects just naturally have a different eating style than the adhering subjects. The results can also be skewed by the personal habits of each user. The only way to remove that possible error is to collect more data and perform more studies.

Ideally the application would collet user eating data as the user ate their meals. With the harnessing of each user’s data, future projects can come up with better bite iterations tailored to each specific user. Machine learning algorithms could be used to teach the application, with the use of extensive amounts of data, a way to select the perfect bite iteration for each meal for each specific kind of user.

We use Gantt Chart during our implementation to keep track with the progress.

• iOS Developing (Swift)
• UI Design
• Data Analysis
• Metabolism mechanism

Editor: Linh Le