{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T22:10:48Z","timestamp":1761948648075,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T00:00:00Z","timestamp":1592179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["5U01CA135133-04"],"award-info":[{"award-number":["5U01CA135133-04"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019 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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s20123380","type":"journal-article","created":{"date-parts":[[2020,6,15]],"date-time":"2020-06-15T12:16:57Z","timestamp":1592223417000},"page":"3380","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["DietSensor: Automatic Dietary Intake Measurement Using Mobile 3D Scanning Sensor for Diabetic Patients"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3618-9736","authenticated-orcid":false,"given":"Sepehr","family":"Makhsous","sequence":"first","affiliation":[{"name":"Sensors 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"}]},{"given":"Mukund","family":"Bharadwaj","sequence":"additional","affiliation":[{"name":"Sensors 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":"https:\/\/orcid.org\/0000-0002-5929-8054","authenticated-orcid":false,"given":"Benjamin E.","family":"Atkinson","sequence":"additional","affiliation":[{"name":"Department of Health Services, Box 357660, School of Public Health, The University of Washington, Seattle, WA 98195, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6347-7450","authenticated-orcid":false,"given":"Igor V.","family":"Novosselov","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, The University of Washington, 3900 E Stevens Way NE, Seattle, WA 98195, USA"}]},{"given":"Alexander V.","family":"Mamishev","sequence":"additional","affiliation":[{"name":"Sensors 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"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,15]]},"reference":[{"key":"ref_1","unstructured":"(2017). National Diabetes Statistics Report, 2017, Centers for Disease Control and Prevention, US Department of Health and Human Services."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.mpmed.2014.09.005","article-title":"What is diabetes?","volume":"42","author":"Egan","year":"2014","journal-title":"Medicine"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.clnu.2011.11.001","article-title":"Malnutrition and its impact on cost of hospitalization, length of stay, readmission and 3-year mortality","volume":"31","author":"Lim","year":"2012","journal-title":"Clin. Nutr."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Guzsvinecz, T., Szucs, V., and Sik-Lanyi, C. (2019). Suitability of the Kinect Sensor and Leap Motion Controller\u2014A Literature Review. Sensors, 19.","DOI":"10.3390\/s19051072"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chan, T.O., Lichti, D.D., Jahraus, A., Esfandiari, H., Lahamy, H., Steward, J., and Glanzer, M. (2018). An Egg Volume Measurement System Based on the Microsoft Kinect. Sensors, 18.","DOI":"10.3390\/s18082454"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wasenm\u00fcller, O., and Stricker, D. (2017). Comparison of Kinect V1 and V2 Depth Images in Terms of Accuracy and Precision, Springer International Publishing.","DOI":"10.1007\/978-3-319-54427-4_3"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/TCI.2015.2510506","article-title":"A Comparative Error Analysis of Current Time-of-Flight Sensors","volume":"2","author":"Placht","year":"2016","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fang, S., Shao, Z., Kerr, D.A., Boushey, C.J., and Zhu, F. (2019). An End-to-End Image-Based Automatic Food Energy Estimation Technique Based on Learned Energy Distribution Images: Protocol and Methodology. Nutrients, 11.","DOI":"10.3390\/nu11040877"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fang, S., Zhu, F., Boushey, C.J., and Delp, E.J. (2017, January 14\u201316). The use of co-occurrence patterns in single image based food portion estimation. Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montreal, QC, Canada.","DOI":"10.1109\/GlobalSIP.2017.8308685"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.pmcj.2011.07.003","article-title":"DietCam: Automatic dietary assessment with mobile camera phones","volume":"8","author":"Kong","year":"2012","journal-title":"Pervasive Mob. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1109\/TMM.2016.2642792","article-title":"Two-View 3D Reconstruction for Food Volume Estimation","volume":"19","author":"Dehais","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Makhsous, S., Mohammad, H.M., Schenk, J.M., Mamishev, A.V., and Kristal, A.R. (2019). A Novel Mobile Structured Light System in Food 3D Reconstruction and Volume Estimation. Sensors, 19.","DOI":"10.3390\/s19030564"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ayaz, S.M., Khan, D., and Kim, M.Y. (2018). Three-Dimensional Registration for Handheld Profiling Systems Based on Multiple Shot Structured Light. Sensors, 18.","DOI":"10.3390\/s18041146"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Peng, T., Zhang, Z., Song, Y., Chen, F., and Zeng, D. (2019). Portable System for Box Volume Measurement Based on Line-Structured Light Vision and Deep Learning. Sensors, 19.","DOI":"10.3390\/s19183921"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1109\/TII.2019.2942831","article-title":"Point2Volume: A Vision-Based Dietary Assessment Approach Using View Synthesis","volume":"16","author":"Lo","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gao, A., Lo, F.P., and Lo, B. (2018, January 4\u20137). Food volume estimation for quantifying dietary intake with a wearable camera. Proceedings of 2018 IEEE 15th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Las Vegas, NV, USA.","DOI":"10.1109\/BSN.2018.8329671"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e2014009","DOI":"10.4178\/epih\/e2014009","article-title":"Dietary assessment methods in epidemiologic studies","volume":"36","author":"Shim","year":"2014","journal-title":"Epidemiol. Health"},{"key":"ref_18","unstructured":"(2020, May 30). Dietary Assessment Primer, 24-Hour Dietary Recall (24HR) At A Glance, Available online: https:\/\/dietassessmentprimer.cancer.gov\/."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1016\/j.jand.2012.04.016","article-title":"The Automated Self-Administered 24-Hour Dietary Recall (ASA24): A Resource for Researchers, Clinicians, and Educators from the National Cancer Institute","volume":"112","author":"Subar","year":"2012","journal-title":"J. Acad. Nutr. Diet."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/j.jada.2004.01.006","article-title":"Accuracy of reporting dietary intake using various portion-size aids in-person and via telephone","volume":"104","author":"Godwin","year":"2004","journal-title":"J. Am. Diet. Assoc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/ejcn.2013.242","article-title":"Under-reporting remains a key limitation of self-reported dietary intake: An analysis of the 2008\/09 New Zealand Adult Nutrition Survey","volume":"68","author":"Gemming","year":"2014","journal-title":"Eur. J. Clin. Nutr."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.nut.2018.05.003","article-title":"The use of a food logging app in the naturalistic setting fails to provide accurate measurements of nutrients and poses usability challenges","volume":"57","author":"Chen","year":"2019","journal-title":"Nutrition"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/1475-2891-10-60","article-title":"Dietary behaviors related to cancer prevention among pre-adolescents and adolescents: The gap between recommendations and reality","volume":"10","author":"Holman","year":"2011","journal-title":"Nutr. J."},{"key":"ref_24","unstructured":"(2020, March 14). Structure by Occipital. Available online: https:\/\/structure.io\/."},{"key":"ref_25","unstructured":"(2020, March 14). Autodesk Meshmixer. Available online: http:\/\/www.meshmixer.com\/."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1515\/amcs-2016-0063","article-title":"A comparison of hole-filling methods in 3D","volume":"26","author":"Salamanca","year":"2016","journal-title":"Int. J. Appl. Math. Comput. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1007\/s00371-007-0167-y","article-title":"A robust hole-filling algorithm for triangular mesh","volume":"23","author":"Zhao","year":"2007","journal-title":"Visual Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3605","DOI":"10.1002\/nme.1620372103","article-title":"The advancing-front mesh generation method revisited","volume":"37","author":"George","year":"1994","journal-title":"Int. J. Numer. Methods Eng."},{"key":"ref_29","unstructured":"Nutrition Coordinating Center (NCC) (2020, March 04). Available online: http:\/\/www.ncc.umn.edu\/."},{"key":"ref_30","unstructured":"(2020, March 04). SELF Nutrition Data. Available online: https:\/\/nutritiondata.self.com\/."},{"key":"ref_31","unstructured":"(2020, March 04). FatSecret. Available online: https:\/\/www.fatsecret.com\/calories-nutrition\/."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2567","DOI":"10.3945\/jn.116.237271","article-title":"The Use of Digital Images in 24-Hour Recalls May Lead to Less Misestimation of Portion Size Compared with Traditional Interviewer-Administered Recalls","volume":"146","author":"Kirkpatrick","year":"2016","journal-title":"J. Nutr."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.jada.2009.10.007","article-title":"Assessment of the Accuracy of Portion Size Reports Using Computer-Based Food Photographs Aids in the Development of an Automated Self-Administered 24-Hour Recall","volume":"110","author":"Subar","year":"2010","journal-title":"J. Am. Diet. Assoc."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Feng, C., Liang, J., Ren, M., Qiao, G., Lu, W., and Liu, S. (2020). A Fast Hole-Filling Method for Triangular Mesh in Additive Repair. Appl. Sci., 10.","DOI":"10.3390\/app10030969"},{"key":"ref_35","unstructured":"(2020, March 16). What is DepthVision Camera on Galaxy Note10+?. Available online: https:\/\/www.samsung.com\/global\/galaxy\/what-is\/depthvision-camera\/."},{"key":"ref_36","unstructured":"(2020). Apple Unveils New iPad Pro with Breakthrough LiDAR Scanner and Brings Trackpad Support to iPadOS, Apple Inc.. Available online: https:\/\/www.apple.com\/newsroom\/2020\/03\/apple-unveils-new-ipad-pro-with-lidar-scanner-and-trackpad-support-in-ipados\/."},{"key":"ref_37","unstructured":"(2020, March 16). HONOR View20. Available online: https:\/\/www.hihonor.com\/global\/products\/smartphone\/honorview20\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3380\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:39:14Z","timestamp":1760175554000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3380"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,15]]},"references-count":37,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20123380"],"URL":"https:\/\/doi.org\/10.3390\/s20123380","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,6,15]]}}}