Tracking and counting motion for monitoring food intake based on depth sensor
Obesity has been a serious health concern among people. Moreover, obesity continues to be a serious public health concern in Malaysia and continuing to rise. Nearly half of Malaysians are overweight. Most of the dietary approaches are not tracking and detecting the right calorie intake for wei...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/1041/2/24p%20MUHAMMAD%20FUAD%20KASSIM.pdf http://eprints.uthm.edu.my/1041/1/MUHAMMAD%20FUAD%20KASSIM%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/1041/3/MUHAMMAD%20FUAD%20KASSIM%20WATERMARK.pdf |
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| Summary: | Obesity has been a serious health concern among people. Moreover, obesity continues
to be a serious public health concern in Malaysia and continuing to rise. Nearly half of
Malaysians are overweight. Most of the dietary approaches are not tracking and
detecting the right calorie intake for weight loss, but currently used tools such as food
diaries require users to manually record and track the food calories, making them
difficult to be utilized for daily use. Therefore, this project developed a new tool that
counts the food intake by monitoring eating motion movement of caloric intake to
overcome health issues. The food intake counting method showed a good significance
that can lead to a successful weight loss by simply monitoring the food intake taken
during eating. The device used was Kinect Xbox One which used a depth camera to
detect the motion of a person’s gesture and posture during food intake. Previous studies
have shown that most of the methods used to count food intake device is worn device
type. The recent trend is now going towards non-wearable devices due to the difficulty
when wearing devices and it has high false alarm ratio. The proposed system gets data
from the Kinect camera and monitors the gesture of the user while eating. Then, the
gesture data is collected to be recognized and it will start counting the food intake
taken by the user. The system recognizes the patterns of the food intake from the user
by following the algorithm design in this thesis to analyze the gesture of the basic
eating type and the system get an average accuracy of 96.2%. This system can help
people who are trying to follow a proper way to avoid being overweight or having
eating disorders by monitoring their meal intake and controlling their eating rate. |
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