Evidence map›Paper›PMID 32549356›Full record

ArticleSensors (Basel, Switzerland)2020

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

Sepehr Makhsous, Mukund Bharadwaj, Benjamin E Atkinson, Igor V Novosselov, Alexander V Mamishev

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Sepehr MakhsousSensors Energy and Automation Laboratory (SEAL), Department of Electrical and Computer Engineering, The University of Washington, Paul Allen Center, 185 E Stevens Way NE AE100R, Seattle, WA 98195, USA.ORCID 0000-0002-3618-9736
Mukund BharadwajSensors Energy and Automation Laboratory (SEAL), Department of Electrical and Computer Engineering, The University of Washington, Paul Allen Center, 185 E Stevens Way NE AE100R, Seattle, WA 98195, USA.
Benjamin E AtkinsonDepartment of Health Services, Box 357660, School of Public Health, The University of Washington, Seattle, WA 98195, USA.ORCID 0000-0002-5929-8054
Igor V NovosselovNovosselov Research Group (NRG) Laboratory, Department of Mechanical Engineering, Department of Environmental & Occupational Health Sciences (DEOHS), The University of Washington, 3900 E Stevens Way NE, Seattle, WA 98195, USA.ORCID 0000-0002-6347-7450
Alexander V MamishevSensors Energy and Automation Laboratory (SEAL), Department of Electrical and Computer Engineering, The University of Washington, Paul Allen Center, 185 E Stevens Way NE AE100R, Seattle, WA 98195, USA.

Funding

NIH HHS 5U01CA135133-04
6 · The paper itself

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.

Indexed as

Diabetes MellitusDietEatingWearable Electronic DevicesHumansMealsNutritional Statusdiabetes treatment assistancedietary measurementfood image processingfood scanningIR sensor

Identifiers

PMID32549356
PMCPMC7349497

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.