Tracking and Counting Motion for Monitoring Food Intake Based-On Depth Sensor and UDOO Board: A Comprehensive Review

Kassim, Muhammad Fuad bin; Mohd, Mohd Norzali Haji

doi:10.1088/1757-899x/226/1/012089

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…In this project, a new method was used to measure food intake. The food counting is measured using wrist joint motion during eating using depth-sensing cameras which is Kinect Xbox One [8][9][10][11][12]. By detecting a characteristic pattern, it can identify when a food intake has been taken.…”

Section: Issn: 2302-9285 mentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Issn: 2302-9285 mentioning

confidence: 99%

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

et al. 2020

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“…If the privacy of subjects is not a priority, exploiting the RGB information could be useful to monitor and discriminate the subjects’ hand and face gesture while eating, like in [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Food Intake Actions Detection: An Improved Algorithm Toward Real-Time Analysis

2020

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With the increase in life expectancy, one of the most important topic for scientific research, especially for the elderly, is good nutrition. In particular, with an advanced age and health issues because disorders such as Alzheimer and dementia, monitoring the subjects’ dietary habits to avoid excessive or poor nutrition is a critical role. Starting from an application aiming to monitor the food intake actions of people during a meal, already shown in a previously published paper, the present work describes some improvements that are able to make the application work in real time. The considered solution exploits the Kinect v1 device that can be installed on the ceiling, in a top-down view in an effort to preserve privacy of the subjects. The food intake actions are estimated from the analysis of depth frames. The innovations introduced in this document are related to the automatic identification of the initial and final frame for the detection of food intake actions, and to the strong revision of the procedure to identify food intake actions with respect to the original work, in order to optimize the performance of the algorithm. Evaluation of the computational effort and system performance compared to the previous version of the application has demonstrated a possible real-time applicability of the solution presented in this document.

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“…It can later be extended to more difficult gestures. The application then transforms the recognized hand gestures into signals to control the drone [2,[10][11][12][13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Flying Drone Controller by Hand Gesture Using Leap Motion

Mutalib¹

2020

IJATCSE

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The Leap Motion is the mechanism to Flying Drone Controller by Hand Gesture Using Leap Motion. It has a sensor for hand gesture to fly a drone. This undertaking consists of a few hardware and software that are microcontroller Arduino, drone-like SYMA X5SC Explorer 2, Microsoft Visual Studio 2019, gesture sensor Leap motion and digital to analog MCP 4725. Leap Motion is taught by the manual motion and the Arduino instruction. Arduino receives the Leap Motion instructions, and the remote controller receives data from Arduino and sends signal to a drone immediately. Based on the hand gesture instruction, Drone flies into the air. There are three instructional conditions: throttle, pitch and roll. This system is designed to create a hand gesture instruction and to develop the program code of the features of the Leap Mo-tion using the Microsoft Visual Studio 2019. This technology has been explored and researched and many new things can be learned. Leap Motion was selected to operate a drone with a touchless condition for this task. There were three purposes in this task after investigations of the control system of hand gesture were made, particularly for drones. The planning is designed to ensure that the system fulfills its goals. It is suitable for farming, photography and military applications. Therefore, drone handling can be improved in future without shifting the key or pressing any control button.

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Tracking and Counting Motion for Monitoring Food Intake Based-On Depth Sensor and UDOO Board: A Comprehensive Review

Cited by 6 publications

References 18 publications

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

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

Food Intake Actions Detection: An Improved Algorithm Toward Real-Time Analysis

Flying Drone Controller by Hand Gesture Using Leap Motion

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