DietSensor: Automatic Dietary Intake Measurement Using Mobile 3D Scanning Sensor for Diabetic Patients

Abstract

Diabetes is a global epidemic that impacts millions of people every year. Enhanced dietary assessment techniques are critical for maintaining a healthy life for a diabetic patient. Moreover, hospitals must monitor their diabetic patients’ food intake to prescribe a certain amount of insulin. Malnutrition significantly increases patient mortality, the duration of the hospital stay, and, ultimately, medical costs. Currently, hospitals are not fully equipped to measure and track a patient’s nutritional intake, and the existing solutions require an extensive user input, which introduces a lot of human errors causing endocrinologists to overlook the measurement. This paper presents DietSensor, a wearable three-dimensional (3D) measurement system, which uses an over the counter 3D camera to assist the hospital personnel with measuring a patient’s nutritional intake. The structured environment of the hospital provides the opportunity to have access to the total nutritional data of any meal prepared in the kitchen as a cloud database. DietSensor uses the 3D scans and correlates them with the hospital kitchen database to calculate the exact consumed nutrition by the patient. The system was tested on twelve volunteers with no prior background or familiarity with the system. The overall calculated nutrition from the DietSensor phone application was compared with the outputs from the 24-h dietary recall (24HR) web application and MyFitnessPal phone application. The average absolute error on the collected data was 73%, 51%, and 33% for the 24HR, MyFitnessPal, and DietSensor systems, respectively.

Evaluation of a Novel Artificial Intelligence System to Monitor and Assess Energy and Macronutrient Intake in Hospitalised Older Patients

“…In our previous study, we had seven food components and five plate types, there was no pretraining, and the meals had to be weighed. Furthermore, in [31], the authors use a system with a 3D scanning sensor on top of a smartphone to segment and estimate the volume of the food components and then calculate the nutritional intake based on the database of a hospital's kitchen. In their study, twelve participants split into groups with different levels of knowledge regarding the meal (given the exact recipe, given a list of recipes, no information on the recipe) were recruited to evaluate the system's performance.…”

A Systematic Review of Sensor-Based Methodologies for Food Portion Size Estimation

“…Recent developments in sensor technology and methodology have enabled researchers to evaluate latest sensors such as off-the-shelf 3D scanners [63]- [66] and latest methodology such as deep learning [67]- [71] in FPSE. Another study used short-range depth cameras to determine FPS [72].…”

A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation

“…Although food recognition has been extensively studied using deep learning techniques [11][12][13][14][15][16][17], estimating food volume from images remains a challenging problem [9][10][11]18]. Several sensor-based approaches have been reported [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. A special imaging sensor called a depth sensor has been used to produce depth on a per-pixel basis from which food volume can be estimated [22][23][24][25][26].…”

“…Several sensor-based approaches have been reported [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. A special imaging sensor called a depth sensor has been used to produce depth on a per-pixel basis from which food volume can be estimated [22][23][24][25][26]. Another effective approach uses a pair of stereo cameras separated by a distance.…”