A non-invasive and non-wearable food intake monitoring system based on depth sensor

Abstract

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 in this project 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 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.

Fluid Intake Monitoring Systems for the Elderly: A Review of the Literature

“…Drinking events were classified with 89% accuracy using a Dynamic Time Warping (DTW) [ 97 ]. Kassim et al and Cunha et al also used a Kinect to monitor wrist joint motion and find the number of bites and drinks consumed [ 98 , 99 ]. They used a single frontal view, which can lead to occlusion issues [ 99 ].…”

Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature Review

“…Similarly, Android's FaceDetector class was used in [33] to eliminate images with visible human faces. Some studies only relied on depth information from RGB-D cameras to reduce the concern of privacy [51][52][53][54], which could suffer from a high false positive rate [52], low accuracy (<90%) [54] or up to 148.8 mm error on mean distance [53]. Hence, algorithms with better performance tracking body movement and recognizing intake activities based only on depth information were needed.…”

Contactless Drink Intake Monitoring Using Depth Data

“…Kassim et al used a Kinect in front of the person to determined intake events during a meal to predict the calories consumed. They achieved an overall accuracy of 96%, however it was only tested on one subject [13]. Hondori et al fused a Kinect with a wrist inertial sensor to detect eating and drinking, however this was only a pilot study with 1 participant [14].…”