{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T20:18:55Z","timestamp":1778876335116,"version":"3.51.4"},"reference-count":23,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T00:00:00Z","timestamp":1662508800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Canadian Institutes of Health Research (CIHR) Foundation","award":["FDN-148450"],"award-info":[{"award-number":["FDN-148450"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Dehydration is a common, serious issue among older adults. It is important to drink fluid to prevent dehydration and the complications that come with it. As many older adults forget to drink regularly, there is a need for an automated approach, tracking intake throughout the day with limited user interaction. The current literature has used vision-based approaches with deep learning models to detect drink events; however, most use static frames (2D networks) in a lab-based setting, only performing eating and drinking. This study proposes a 3D convolutional neural network using video segments to detect drinking events. In this preliminary study, we collected data from 9 participants in a home simulated environment performing daily activities as well as eating and drinking from various containers to create a robust environment and dataset. Using state-of-the-art deep learning models, we trained our CNN using both static images and video segments to compare the results. The 3D model attained higher performance (compared to 2D CNN) with F1 scores of 93.7% and 84.2% using 10-fold and leave-one-subject-out cross-validations, respectively.<\/jats:p>","DOI":"10.3390\/s22186747","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"6747","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automated Fluid Intake Detection Using RGB Videos"],"prefix":"10.3390","volume":"22","author":[{"given":"Rachel","family":"Cohen","sequence":"first","affiliation":[{"name":"KITE Research Institute, Toronto Rehabilitation Hospital, University Health Network; 550 University Ave, Toronto, ON M5G2A2, Canada"},{"name":"Institute of Biomedical Engineering, University of Toronto, 164 College St, Toronto, ON M5S 3G9, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4821-7650","authenticated-orcid":false,"given":"Geoff","family":"Fernie","sequence":"additional","affiliation":[{"name":"KITE Research Institute, Toronto Rehabilitation Hospital, University Health Network; 550 University Ave, Toronto, ON M5G2A2, Canada"},{"name":"Institute of Biomedical Engineering, University of Toronto, 164 College St, Toronto, ON M5S 3G9, Canada"},{"name":"Department of Surgery, University of Toronto, 149 College Street, Toronto, ON M5T 1P5, Canada"}]},{"given":"Atena","family":"Roshan Fekr","sequence":"additional","affiliation":[{"name":"KITE Research Institute, Toronto Rehabilitation Hospital, University Health Network; 550 University Ave, Toronto, ON M5G2A2, Canada"},{"name":"Institute of Biomedical Engineering, University of Toronto, 164 College St, Toronto, ON M5S 3G9, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1067\/mgn.2000.107135","article-title":"Dehydration: Hazards and Benefits","volume":"21","author":"Bennett","year":"2000","journal-title":"Geriatr. Nur."},{"key":"ref_2","first-page":"6","article-title":"The Pathophysiology of Fluid and Electrolyte Balance in the Older Adult Surgical Patient","volume":"33","author":"Sahota","year":"2014","journal-title":"Clin. Nutr. Edinb. Scotl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1056\/NEJM198409203111202","article-title":"Reduced Thirst after Water Deprivation in Healthy Elderly Men","volume":"311","author":"Phillips","year":"1984","journal-title":"N. Engl. J. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1111\/j.1365-2702.1993.tb00156.x","article-title":"Elderly Women\u2019s Feelings about Being Urinary Incontinent, Using Napkins and Being Helped by Nurses to Change Napkins","volume":"2","author":"Birgersson","year":"1993","journal-title":"J. Clin. Nurs."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"121","DOI":"10.3945\/ajcn.115.119925","article-title":"Water-Loss (Intracellular) Dehydration Assessed Using Urinary Tests: How Well Do They Work? Diagnostic Accuracy in Older People","volume":"104","author":"Hooper","year":"2016","journal-title":"Am. J. Clin. Nutr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"575S","DOI":"10.1080\/07315724.2007.10719661","article-title":"Assessing Hydration Status: The Elusive Gold Standard","volume":"26","author":"Armstrong","year":"2007","journal-title":"J. Am. Coll. Nutr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"S22","DOI":"10.1111\/j.1753-4887.2005.tb00151.x","article-title":"Strategies for Ensuring Good Hydration in the Elderly","volume":"63","author":"Ferry","year":"2005","journal-title":"Nutr. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cohen, R., Fernie, G., and Roshan Fekr, A. (2021). Fluid Intake Monitoring Systems for the Elderly: A Review of the Literature. Nutrients, 13.","DOI":"10.3390\/nu13062092"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.appet.2015.05.019","article-title":"The Use of a Wearable Camera to Capture and Categorise the Environmental and Social Context of Self-Identified Eating Episodes","volume":"92","author":"Gemming","year":"2015","journal-title":"Appetite"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.1007\/s00394-020-02380-4","article-title":"Using Wearable Cameras to Monitor Eating and Drinking Behaviours during Transport Journeys","volume":"60","author":"Davies","year":"2020","journal-title":"Eur. J. Nutr."},{"key":"ref_11","unstructured":"Doulah, A.B.M.S.U. (2018). A Wearable Sensor System for Automatic Food Intake Detection and Energy Intake Estimation in Humans. [Ph.D. Thesis, University of Alabama Libraries]."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Raju, V., and Sazonov, E. (2019, January 11\u201314). Processing of Egocentric Camera Images from a Wearable Food Intake Sensor. Proceedings of the 2019 SoutheastCon, Huntsville, AL, USA.","DOI":"10.1109\/SoutheastCon42311.2019.9020284"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1727","DOI":"10.1109\/JBHI.2019.2942845","article-title":"Learning Deep Representations for Video-Based Intake Gesture Detection","volume":"24","author":"Rouast","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_14","unstructured":"Heydarian, H., Adam, M.T.P., Burrows, T., and Rollo, M.E. (2021). Exploring Score-Level and Decision-Level Fusion of Inertial and Video Data for Intake Gesture Detection. IEEE Access."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Iosifidis, A., Marami, E., Tefas, A., and Pitas, I. (2012, January 27). Eating and Drinking Activity Recognition Based on Discriminant Analysis of Fuzzy Distances and Activity Volumes. Proceedings of the 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan.","DOI":"10.1109\/ICASSP.2012.6288350"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bi, S., and Kotz, D. (2021). Eating Detection with a Head-Mounted Video Camera. Comput. Sci. Tech. Rep., Available online: https:\/\/digitalcommons.dartmouth.edu\/cs_tr\/384\/.","DOI":"10.1109\/ICHI54592.2022.00021"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chang, M.-J., Hsieh, J.-T., Fang, C.-Y., and Chen, S.-W. (2019, January 27). A Vision-Based Human Action Recognition System for Moving Cameras Through Deep Learning. Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning; Association for Computing Machinery, New York, NY, USA.","DOI":"10.1145\/3372806.3372815"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Carreira, J., and Zisserman, A. (2017, January 21\u201326). Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.502"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., and Serre, T. HMDB: A Large Video Database for Human Motion Recognition. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain, 6\u201313 November 2011.","DOI":"10.1109\/ICCV.2011.6126543"},{"key":"ref_20","unstructured":"Soomro, K., Zamir, A.R., and Shah, M. (2012). UCF101: A Dataset of 101 Human Actions Classes from Videos in the Wild. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wu, K., He, S., Fernie, G., and Roshan Fekr, A. (2020). Deep Neural Network for Slip Detection on Ice Surface. Sensors, 20.","DOI":"10.3390\/s20236883"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.procs.2016.09.130","article-title":"Automatic Meal Intake Monitoring Using Hidden Markov Models","volume":"100","author":"Costa","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6747\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:24:43Z","timestamp":1760142283000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/18\/6747"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,7]]},"references-count":23,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22186747"],"URL":"https:\/\/doi.org\/10.3390\/s22186747","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,7]]}}}