ArticleSensors (Basel, Switzerland)2020
DietSensor: Automatic Dietary Intake Measurement Using Mobile 3D Scanning Sensor for Diabetic Patients.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review.Annals of medicine · 2023Pooled it
- The Impact of Dietary Sugars and Saturated Fats on Body and Liver Fat in a Healthcare Worker Population.Nutrients · 2025Article
- Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools-a scoping review of potential applications.Frontiers in nutrition · 2025Review
- Continuity, Considerations, and Future Directions for the Healthy Eating Index-Toddlers-2020.Journal of the Academy of Nutrition and Dietetics · 2023Article
- A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation.Sensors (Basel, Switzerland) · 2022Article
- Evaluation of a Novel Artificial Intelligence System to Monitor and Assess Energy and Macronutrient Intake in Hospitalised Older Patients.Nutrients · 2021Article
- An Assistive Technology Solution for User Activity Monitoring Exploiting Passive RFID.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
5 authors.
Funding
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.
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