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.

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

Gambi, E
;
Ricciuti, M;De Santis, A
2020-01-01

Abstract

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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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/282726
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