{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T18:39:22Z","timestamp":1761676762088,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2019,6,22]],"date-time":"2019-06-22T00:00:00Z","timestamp":1561161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007434","name":"Ag\u00eancia Nacional de Inova\u00e7\u00e3o","doi-asserted-by":"publisher","award":["NORTE-01-0145-FEDER-000026"],"award-info":[{"award-number":["NORTE-01-0145-FEDER-000026"]}],"id":[{"id":"10.13039\/501100007434","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The increasingly aging society in developed countries has raised attention to the role of technology in seniors\u2019 lives, namely concerning isolation-related issues. Independent seniors that live alone frequently neglect meals, hydration and proper medication-taking behavior. This work aims at eating and drinking recognition in free-living conditions for triggering smart reminders to autonomously living seniors, keeping system design considerations, namely usability and senior-acceptance criteria, in the loop. To that end, we conceived a new dataset featuring accelerometer and gyroscope wrist data to conduct the experiments. We assessed the performance of a single multi-class classification model when compared against several binary classification models, one for each activity of interest (eating vs. non-eating; drinking vs. non-drinking). Binary classification models performed consistently better for all tested classifiers (k-NN, Naive Bayes, Decision Tree, Multilayer Perceptron, Random Forests, HMM). This evidence supported the proposal of a semi-hierarchical activity recognition algorithm that enabled the implementation of two distinct data stream segmentation techniques, the customization of the classification models of each activity of interest and the establishment of a set of restrictions to apply on top of the classification output, based on daily evidence. An F1-score of 97% was finally attained for the simultaneous recognition of eating and drinking in an all-day acquisition from one young user, and 93% in a test set with 31 h of data from 5 different unseen users, 2 of which were seniors. These results were deemed very promising towards solving the problem of food and fluids intake monitoring with practical systems which shall maximize user-acceptance.<\/jats:p>","DOI":"10.3390\/s19122803","type":"journal-article","created":{"date-parts":[[2019,6,24]],"date-time":"2019-06-24T02:37:40Z","timestamp":1561343860000},"page":"2803","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Eating and Drinking Recognition in Free-Living Conditions for Triggering Smart Reminders"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5617-6126","authenticated-orcid":false,"given":"Diana","family":"Gomes","sequence":"first","affiliation":[{"name":"Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2471-2833","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Mendes-Moreira","sequence":"additional","affiliation":[{"name":"LIAAD-INESC TEC, 4200-465 Porto, Portugal"},{"name":"Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]},{"given":"In\u00eas","family":"Sousa","sequence":"additional","affiliation":[{"name":"Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal"}]},{"given":"Joana","family":"Silva","sequence":"additional","affiliation":[{"name":"Fraunhofer Portugal AICOS, 4200-135 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,22]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Anderez, D.O., Appiah, K., Lotfi, A., and Langesiepen, C. (2017, January 21\u201323). A hierarchical approach towards activity recognition. Proceedings of the 10th International Conference on Pervasive Technologies Related to Assistive Environments. ACM, Rhodes, Greece.","key":"ref_1","DOI":"10.1145\/3056540.3076194"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"S21","DOI":"10.1016\/j.gerinurse.2015.02.019","article-title":"Acceptability of wristband activity trackers among community dwelling older adults","volume":"36","author":"Hathaway","year":"2015","journal-title":"Geriatr. Nurs."},{"doi-asserted-by":"crossref","unstructured":"Holzinger, A., Searle, G., Pr\u00fcckner, S., Steinbach-Nordmann, S., Kleinberger, T., Hirt, E., and Temnitzer, J. (2010, January 22\u201325). Perceived usefulness among elderly people: Experiences and lessons learned during the evaluation of a wrist device. Proceedings of the 2010 4th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth). IEEE, Munich, Germany.","key":"ref_3","DOI":"10.4108\/ICST.PERVASIVEHEALTH2010.8912"},{"doi-asserted-by":"crossref","unstructured":"Rasche, P., Wille, M., Theis, S., Sch\u00e4efer, K., Schlick, C.M., and Mertens, A. (2015, January 26\u201328). Activity tracker and elderly. Proceedings of the 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT\/IUCC\/DASC\/PICOM), Liverpool, UK.","key":"ref_4","DOI":"10.1109\/CIT\/IUCC\/DASC\/PICOM.2015.211"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1007\/s00779-010-0293-9","article-title":"Preprocessing techniques for context recognition from accelerometer data","volume":"14","author":"Figo","year":"2010","journal-title":"Pers. Ubiquitous Comput."},{"doi-asserted-by":"crossref","unstructured":"Thomaz, E., Essa, I., and Abowd, G.D. (2015, January 7\u201311). A practical approach for recognizing eating moments with wrist-mounted inertial sensing. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, Osaka, Japan.","key":"ref_6","DOI":"10.1145\/2750858.2807545"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1109\/JBHI.2013.2282471","article-title":"Detecting periods of eating during free-living by tracking wrist motion","volume":"18","author":"Dong","year":"2014","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1145\/3130902","article-title":"EarBit: Using wearable sensors to detect eating episodes in unconstrained environments","volume":"1","author":"Bedri","year":"2017","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"unstructured":"Amft, O., Bannach, D., Pirkl, G., Kreil, M., and Lukowicz, P. (April, January 29). Towards wearable sensing-based assessment of fluid intake. Proceedings of the PerCom Workshops, Mannheim, Germany.","key":"ref_10"},{"unstructured":"Kozina, S., Lustrek, M., and Gams, M. (2011, January 16\u201322). Dynamic signal segmentation for activity recognition. Proceedings of the International Joint Conference on Artificial Intelligence, Barcelona, Spain.","key":"ref_11"},{"doi-asserted-by":"crossref","unstructured":"Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., and Havinga, P.J. (2016). Complex human activity recognition using smartphone and wrist-worn motion sensors. Sensors, 16.","key":"ref_12","DOI":"10.3390\/s16040426"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1109\/JBHI.2014.2329137","article-title":"Improving the recognition of eating gestures using intergesture sequential dependencies","volume":"19","author":"Muth","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"253","DOI":"10.3233\/AIS-2011-0110","article-title":"Video based technology for ambient assisted living: A review of the literature","volume":"3","author":"Cardinaux","year":"2011","journal-title":"J. Ambient Intell. Smart Environ."},{"doi-asserted-by":"crossref","unstructured":"Bao, L., and Intille, S.S. (2004). Activity recognition from user-annotated acceleration data. Proceedings of International Conference on Pervasive Computing, Springer.","key":"ref_15","DOI":"10.1007\/978-3-540-24646-6_1"},{"unstructured":"Zhu, C. (2011). Hand Gesture and Activity Recognition in Assisted Living through Wearable Sensing and Computing. [Ph.D. Thesis, Faculty of the Graduate College of Oklahoma State University].","key":"ref_16"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1016\/j.eswa.2012.09.004","article-title":"Elderly activities recognition and classification for applications in assisted living","volume":"40","author":"Chernbumroong","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1007\/s00779-011-0455-4","article-title":"Human motion recognition using a wireless sensor-based wearable system","volume":"16","author":"Varkey","year":"2012","journal-title":"Pers. Ubiquitous Comput."},{"unstructured":"Thomaz, E. (2016). Automatic Eating Detection in Real-World Settings with Commodity Sensing. [Ph.D. Thesis, Georgia Institute of Technology].","key":"ref_19"},{"unstructured":"Amft, O., Junker, H., and Troster, G. (2005, January 18\u201321). Detection of eating and drinking arm gestures using inertial body-worn sensors. Proceedings of the 9th IEEE International Symposium on Wearable Computers (ISWC\u201905), Osaka, Japan.","key":"ref_20"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.artmed.2007.11.007","article-title":"Recognition of dietary activity events using on-body sensors","volume":"42","author":"Amft","year":"2008","journal-title":"Artif. Intell. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2010","DOI":"10.1016\/j.patcog.2007.11.016","article-title":"Gesture spotting with body-worn inertial sensors to detect user activities","volume":"41","author":"Junker","year":"2008","journal-title":"Pattern Recognit."},{"doi-asserted-by":"crossref","unstructured":"Lukowicz, P., Ward, J.A., Junker, H., St\u00e4ger, M., Tr\u00f6ster, G., Atrash, A., and Starner, T. (2004). Recognizing workshop activity using body worn microphones and accelerometers. Proceedings of the International Conference on Pervasive Computing, Springer.","key":"ref_23","DOI":"10.1007\/978-3-540-24646-6_2"},{"unstructured":"Lee, C., and Xu, Y. (1996, January 22\u201328). Online, interactive learning of gestures for human\/robot interfaces. Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, MN, USA.","key":"ref_24"},{"doi-asserted-by":"crossref","unstructured":"Gomes, D., and Sousa, I. (2019). Real-Time drink trigger detection in free-living conditions using inertial sensors. Sensors, 19.","key":"ref_25","DOI":"10.3390\/s19092145"},{"key":"ref_26","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"unstructured":"Fraunhofer Portugal AICOS (2016). A Day with Pandlets, Fraunhofer Portugal. Technical Report.","key":"ref_27"},{"unstructured":"Charmant, J. (2012). Kinovea, Available online: https:\/\/www.kinovea.org\/.","key":"ref_28"},{"doi-asserted-by":"crossref","unstructured":"Shen, Y., Muth, E., and Hoover, A. (2016, January 27\u201329). Recognizing eating gestures using context dependent hidden Markov models. Proceedings of the IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), Washington, DC, USA.","key":"ref_29","DOI":"10.1109\/CHASE.2016.9"},{"doi-asserted-by":"crossref","unstructured":"Hanai, Y., Nishimura, J., and Kuroda, T. (2009, January 4\u20137). Haar-like filtering for human activity recognition using 3d accelerometer. Proceedings of the IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, Marco Island, FL, USA.","key":"ref_30","DOI":"10.1109\/DSP.2009.4786008"},{"unstructured":"Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984). Classification and Regression Trees, Routledge.","key":"ref_31"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/TSMC.1972.4309137","article-title":"Asymptotic properties of nearest neighbor rules using edited data","volume":"2","author":"Wilson","year":"1972","journal-title":"IEEE Trans. Syst. Man Cybern."},{"doi-asserted-by":"crossref","unstructured":"Batista, G.E., Prati, R.C., and Monard, M.C. (2005). Balancing strategies and class overlapping. Proceedings of International Symposium on Intelligent Data Analysis, Springer.","key":"ref_34","DOI":"10.1007\/11552253_3"},{"unstructured":"More, A. (2016). Survey of resampling techniques for improving classification performance in unbalanced datasets. arXiv.","key":"ref_35"},{"unstructured":"Statista, T.S.P. (2017, July 12). Time Spent Eating and Drinking by Men and Women in OECD Countries 2016|Statistic, 2016. Available online: https:\/\/www.statista.com\/statistics\/521972\/time-spent-eating-drinking-countries\/.","key":"ref_36"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/12\/2803\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:00:34Z","timestamp":1760187634000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/12\/2803"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,22]]},"references-count":36,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["s19122803"],"URL":"https:\/\/doi.org\/10.3390\/s19122803","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,6,22]]}}}