{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T08:59:25Z","timestamp":1783414765437,"version":"3.54.6"},"reference-count":53,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T00:00:00Z","timestamp":1666224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000865","name":"Bill and Melinda Gates Foundation","doi-asserted-by":"publisher","award":["OPP1171395"],"award-info":[{"award-number":["OPP1171395"]}],"id":[{"id":"10.13039\/100000865","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000865","name":"Bill and Melinda Gates Foundation","doi-asserted-by":"publisher","award":["R56 DK113819"],"award-info":[{"award-number":["R56 DK113819"]}],"id":[{"id":"10.13039\/100000865","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000865","name":"Bill and Melinda Gates Foundation","doi-asserted-by":"publisher","award":["R01DK127310"],"award-info":[{"award-number":["R01DK127310"]}],"id":[{"id":"10.13039\/100000865","id-type":"DOI","asserted-by":"publisher"}]},{"name":"U.S. National Institutes of Health","award":["OPP1171395"],"award-info":[{"award-number":["OPP1171395"]}]},{"name":"U.S. National Institutes of Health","award":["R56 DK113819"],"award-info":[{"award-number":["R56 DK113819"]}]},{"name":"U.S. National Institutes of Health","award":["R01DK127310"],"award-info":[{"award-number":["R01DK127310"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>An unhealthy diet is strongly linked to obesity and numerous chronic diseases. Currently, over two-thirds of American adults are overweight or obese. Although dietary assessment helps people improve nutrition and lifestyle, traditional methods for dietary assessment depend on self-report, which is inaccurate and often biased. In recent years, as electronics, information, and artificial intelligence (AI) technologies advanced rapidly, image-based objective dietary assessment using wearable electronic devices has become a powerful approach. However, research in this field has been focused on the developments of advanced algorithms to process image data. Few reports exist on the study of device hardware for the particular purpose of dietary assessment. In this work, we demonstrate that, with the current hardware design, there is a considerable risk of missing important dietary data owing to the common use of rectangular image screen and fixed camera orientation. We then present two designs of a new camera system to reduce data loss by generating circular images using rectangular image sensor chips. We also present a mechanical design that allows the camera orientation to be adjusted, adapting to differences among device wearers, such as gender, body height, and so on. Finally, we discuss the pros and cons of rectangular versus circular images with respect to information preservation and data processing using AI algorithms.<\/jats:p>","DOI":"10.3390\/s22208006","type":"journal-article","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:34:30Z","timestamp":1666312470000},"page":"8006","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Improved Wearable Devices for Dietary Assessment Using a New Camera System"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7948-9205","authenticated-orcid":false,"given":"Mingui","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA 15260, USA"},{"name":"Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA"},{"name":"Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyan","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangzong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingke","family":"Hou","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiacheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-Hong","family":"Mao","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA"},{"name":"Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15260, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1775","DOI":"10.1016\/S0140-6736(10)61514-0","article-title":"Tackling of unhealthy diets, physical inactivity, and obesity: Health effects and cost-effectiveness","volume":"376","author":"Cecchini","year":"2010","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1016\/S0140-6736(15)00128-2","article-title":"Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990\u20132013: A systematic analysis for the Global Burden of Disease Study 2013","volume":"386","author":"Forouzanfar","year":"2015","journal-title":"Lancet"},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"Gibson, R.S. (2005). Principles of Nutritional Assessment, Oxford University Press.","DOI":"10.1093\/oso\/9780195171693.001.0001"},{"key":"ref_5","unstructured":"Thompson, F.E., and Subar, A.F. (2001). Chapter 1. Dietary Assessment Methodology, Academic Press."},{"key":"ref_6","first-page":"38","article-title":"Dietary assessment methods: Dietary records","volume":"31","author":"Ortega","year":"2015","journal-title":"Nutr. Hosp."},{"key":"ref_7","unstructured":"Willett, W. (2012). 24-hour recall and diet record methods. Nutritional Epidemiology, Oxford University Press. [3rd ed.]."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e11170","DOI":"10.2196\/11170","article-title":"Mobile ecological momentary diet assessment methods for behavioral research: Systematic review","volume":"6","author":"Schembre","year":"2018","journal-title":"JMIR mHealth uHealth"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Wark, P.A., Hardie, L.J., Frost, G.S., Alwan, N.A., Carter, M., Elliott, P., Ford, H.E., Hancock, N., Morris, M.A., and Mulla, U.Z. (2018). Validity of an online 24-h recall tool (myfood24) for dietary assessment in population studies: Comparison with biomarkers and standard interviews. BMC Med., 16.","DOI":"10.1186\/s12916-018-1113-8"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e29","DOI":"10.1017\/jns.2019.20","article-title":"Validity and reliability of an online self-report 24-h dietary recall method (Intake24): A doubly labelled water study and repeated-measures analysis","volume":"8","author":"Foster","year":"2019","journal-title":"J. Nutr. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hasenbohler, A., Denes, L., Blanstier, N., Dehove, H., Hamouche, N., Beer, S., Williams, G., Breil, B., Depeint, F., and Cade, J.E. (2022). Development of an innovative online dietary assessment tool for france: Adaptation of myfood24. Nutrients, 14.","DOI":"10.3390\/nu14132681"},{"key":"ref_13","unstructured":"U.S. Department of Agriculture, Agricultural Research Service (2022, October 01). 2020 USDA Food and Nutrient Database for Dietary Studies 2017\u20132018. Food Surveys Research Group Home Page, \/ba\/bhnrc\/fsrg, Available online: https:\/\/www.ars.usda.gov\/northeast-area\/beltsville-md-bhnrc\/beltsville-human-nutrition-research-center\/food-surveys-research-group\/docs\/fndds-download-databases\/."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"S73","DOI":"10.1017\/S0007114509990602","article-title":"Misreporting of energy and micronutrient intake estimated by food records and 24 hour recalls, control and adjustment methods in practice","volume":"101","author":"Poslusna","year":"2009","journal-title":"Br. J. Nutr."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1079\/PHN2002383","article-title":"Bias in dietary-report instruments and its implications for nutritional epidemiology","volume":"5","author":"Kipnis","year":"2002","journal-title":"Pub. Health Nutr."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.jand.2014.09.015","article-title":"Image-assisted dietary assessment: A systematic review of the evidence","volume":"115","author":"Gemming","year":"2015","journal-title":"J. Acad. Nutr. Diet."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1017\/S0029665116002913","article-title":"New mobile methods for dietary assessment: Review of image-assisted and image-based dietary assessment methods","volume":"76","author":"Boushey","year":"2017","journal-title":"Proc. Nutr. Soc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1007\/s40137-021-00297-3","article-title":"The age of artificial intelligence: Use of digital technology in clinical nutrition","volume":"9","author":"Limketkai","year":"2021","journal-title":"Curr. Surg. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.amepre.2012.11.007","article-title":"Using a wearable camera to increase the accuracy of dietary analysis","volume":"44","author":"Cullen","year":"2013","journal-title":"Am. J. Prev. Med."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Farooq, M., Doulah, A., Parton, J., McCrory, M.A., Higgins, J.A., and Sazonov, E. (2019). Validation of sensor-based food intake detection by multicamera video observation in an unconstrained environment. Nutrients, 11.","DOI":"10.3390\/nu11030609"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3389\/fnut.2017.00031","article-title":"Meal microstructure characterization from sensor-based food intake detection","volume":"4","author":"Doulah","year":"2017","journal-title":"Front. Nutr."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1772","DOI":"10.1109\/TBME.2014.2306773","article-title":"Automatic ingestion monitor: A novel wearable device for monitoring of ingestive behavior","volume":"61","author":"Fontana","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1136\/jamia.2010.005173","article-title":"Ear-worn body sensor network device: An objective tool for functional postoperative home recovery monitoring","volume":"18","author":"Aziz","year":"2011","journal-title":"J. Am. Med. Inf. Assoc."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sun, M., Burke, L.E., Mao, Z.H., Chen, Y., Chen, H.C., Bai, Y., Li, Y., Li, C., and Jia, W. (2014, January 1\u20135). eButton: A wearable computer for health monitoring and personal assistance. Proceedings of the 51st Annual Design Automation Conference, San Francisco, CA, USA.","DOI":"10.1145\/2593069.2596678"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1260\/2040-2295.6.1.1","article-title":"An exploratory study on a chest-worn computer for evaluation of diet, physical activity and lifestyle","volume":"6","author":"Sun","year":"2015","journal-title":"J. Healthc. Eng."},{"key":"ref_26","unstructured":"McCrory, M.A., Sun, M., Sazonov, E., Frost, G., Anderson, A., Jia, W., Jobarteh, M.L., Maitland, K., Steiner, M., and Ghosh, T. (2019, January 8\u201311). Methodology for objective, passive, image- and sensor-based assessment of dietary intake, meal-timing, and food-related activity in Ghana and Kenya. Proceedings of the Annual Nutrition Conference, Baltimore, MD, USA."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chan, V., Davies, A., Wellard-Cole, L., Lu, S., Ng, H., Tsoi, L., Tiscia, A., Signal, L., Rangan, A., and Gemming, L. (2021). Using wearable cameras to assess foods and beverages omitted in 24 hour dietary recalls and a text entry food record app. Nutrients, 13.","DOI":"10.3390\/nu13061806"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"nzaa020","DOI":"10.1093\/cdn\/nzaa020","article-title":"Development and validation of an objective, passive dietary assessment method for estimating food and nutrient intake in households in low- and middle-income countries: A study protocol","volume":"4","author":"Jobarteh","year":"2020","journal-title":"Curr. Dev. Nutr."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1038\/ejcn.2013.156","article-title":"Feasibility of a SenseCam-assisted 24-h recall to reduce under-reporting of energy intake","volume":"67","author":"Gemming","year":"2013","journal-title":"Eur. J. Clin. Nutr."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Alameda-Pineda, X., Ricci, E., and Sebe, N. (2019). Chapter 5-Audio-visual learning for body-worn cameras. Multimodal Behavior Analysis in the Wild, Academic Press.","DOI":"10.1016\/B978-0-12-814601-9.00011-0"},{"key":"ref_31","unstructured":"(2022, October 05). OMNIVISION-Image Sensor. Available online: https:\/\/www.ovt.com\/products\/#image-sensor."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/34.888718","article-title":"A flexible new technique for camera calibration","volume":"22","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Scaramuzza, D., Martinelli, A., and Siegwart, R. (2006, January 9\u201315). A toolbox for easily calibrating omnidirectional cameras. Proceedings of the 2006 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Benjing, China.","DOI":"10.1109\/IROS.2006.282372"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1109\/34.159901","article-title":"Camera calibration with distortion models and accuracy evaluation","volume":"14","author":"Weng","year":"1992","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.isprsjprs.2015.06.005","article-title":"Improved wide-angle, fisheye and omnidirectional camera calibration","volume":"108","author":"Urban","year":"2015","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Scaramuzza, D., Martinelli, A., and Siegwart, R. (2006, January 4\u20137). A flexible technique for accurate omnidirectional camera calibration and structure from motion. Proceedings of the Fourth IEEE International Conference on Computer Vision Systems (ICVS\u201906), New York, NY, USA.","DOI":"10.1109\/ICVS.2006.3"},{"key":"ref_37","unstructured":"Micusik, B., and Pajdla, T. (2003, January 18\u201320). Estimation of omnidirectional camera model from epipolar geometry. Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"875143","DOI":"10.3389\/fnut.2022.875143","article-title":"The food recognition benchmark: Using deep learning to recognize food in images","volume":"9","author":"Mohanty","year":"2022","journal-title":"Front. Nutr."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"25453","DOI":"10.1007\/s11042-021-10916-x","article-title":"A novel deep learning neural network for fast-food image classification and prediction using modified loss function","volume":"80","author":"Lohala","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_40","first-page":"1168","article-title":"Automatic food detection in egocentric images using artificial intelligence technology","volume":"22","author":"Jia","year":"2019","journal-title":"Pub. Health Nutr."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Qiu, J., Lo, F.P., and Lo, B. (2019, January 19\u201322). Assessing individual dietary intake in food sharing scenarios with a 360 camera and deep learning. Proceedings of the 2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Chicago, IL, USA.","DOI":"10.1109\/BSN.2019.8771095"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lo, F.P., Sun, Y., Qiu, J., and Lo, B. (2018). Food volume estimation based on deep learning view synthesis from a single depth map. Nutrients, 10.","DOI":"10.3390\/nu10122005"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Subhi, M.A., and Ali, S.M. (2018, January 3\u20136). A deep convolutional neural network for food detection and recognition. Proceedings of the 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), Sarawak, Malaysia.","DOI":"10.1109\/IECBES.2018.8626720"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Temdee, P., and Uttama, S. (2017, January 15\u201318). Food recognition on smartphone using transfer learning of convolution neural network. Proceedings of the 2017 Global Wireless Summit (GWS), Cape Town, South Africa.","DOI":"10.1109\/GWS.2017.8300490"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, C., Cao, Y., Luo, Y., Chen, G., Vokkarane, V., and Ma, Y. (2016, January 25\u201327). DeepFood: Deep learning-based food image recognition for computer-aided dietary assessment. Proceedings of the International Conference on Smart Homes and Health Telematics, Wuhan, China.","DOI":"10.1007\/978-3-319-39601-9_4"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"108380","DOI":"10.1016\/j.compeleceng.2022.108380","article-title":"Bayesian deep learning for semantic segmentation of food images","volume":"103","author":"Aguilar","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Mezgec, S., and Korousic Seljak, B. (2017). NutriNet: A deep learning food and drink image recognition system for dietary assessment. Nutrients, 9.","DOI":"10.3390\/nu9070657"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kawano, Y., and Yanai, K. (2014, January 13\u201317). Food image recognition with deep convolutional features. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, Seattle, WA, USA.","DOI":"10.1145\/2638728.2641339"},{"key":"ref_49","first-page":"297","article-title":"Image augmentation-based food recognition with convolutional neural networks","volume":"59","author":"Pan","year":"2019","journal-title":"Comput. Mater. Contin."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Rashed, H., Mohamed, E., Sistu, G., Kumar, V.R., Eising, C., El-Sallab, A., and Yogamani, S.K. (2020, January 11). FisheyeYOLO: Object detection on fisheye cameras for autonomous driving. Proceedings of the Machine Learning for Autonomous Driving NeurIPS 2020 Virtual Workshop, Virtual.","DOI":"10.1109\/WACV48630.2021.00232"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Baek, I., Davies, A., Yan, G., and Rajkumar, R.R. (2018, January 26\u201330). Real-time detection, tracking, and classification of moving and stationary objects using multiple fisheye images. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500455"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Goodarzi, P., Stellmacher, M., Paetzold, M., Hussein, A., and Matthes, E. (2019, January 4\u20136). Optimization of a cnn-based object detector for fisheye cameras. Proceedings of the 2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Cairo, Egypt.","DOI":"10.1109\/ICVES.2019.8906325"},{"key":"ref_53","unstructured":"(2022, October 05). Radar Display. Available online: https:\/\/en.wikipedia.org\/wiki\/Radar_display."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/8006\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:58:01Z","timestamp":1760144281000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/20\/8006"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,20]]},"references-count":53,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22208006"],"URL":"https:\/\/doi.org\/10.3390\/s22208006","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,20]]}}}