乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T23:55:06Z","timestamp":1724802906167},"reference-count":80,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T00:00:00Z","timestamp":1615248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100013293","name":"Active and Assisted Living programme","doi-asserted-by":"publisher","award":["AAL-2018-5-120-CP"],"id":[{"id":"10.13039\/100013293","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020","doi-asserted-by":"publisher","award":["769765"],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Understanding people\u2019s eating habits plays a crucial role in interventions promoting a healthy lifestyle. This requires objective measurement of the time at which a meal takes place, the duration of the meal, and what the individual eats. Smartwatches and similar wrist-worn devices are an emerging technology that offers the possibility of practical and real-time eating monitoring in an unobtrusive, accessible, and affordable way. To this end, we present a novel approach for the detection of eating segments with a wrist-worn device and fusion of deep and classical machine learning. It integrates a novel data selection method to create the training dataset, and a method that incorporates knowledge from raw and virtual sensor modalities for training with highly imbalanced datasets. The proposed method was evaluated using data from 12 subjects recorded in the wild, without any restriction about the type of meals that could be consumed, the cutlery used for the meal, or the location where the meal took place. The recordings consist of data from accelerometer and gyroscope sensors. The experiments show that our method for detection of eating segments achieves precision of 0.85, recall of 0.81, and F1-score of 0.82 in a person-independent manner. The results obtained in this study indicate that reliable eating detection using in the wild recorded data is possible with the use of wearable sensors on the wrist.<\/jats:p>","DOI":"10.3390\/s21051902","type":"journal-article","created":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T09:33:51Z","timestamp":1615282431000},"page":"1902","source":"Crossref","is-referenced-by-count":17,"title":["Smartwatch-Based Eating Detection: Data Selection for Machine Learning from Imbalanced Data with Imperfect Labels"],"prefix":"10.3390","volume":"21","author":[{"given":"Simon","family":"Stankoski","sequence":"first","affiliation":[{"name":"Department of Intelligent Systems, Jo\u017eef Stefan Institute, 1000 Ljubljana, Slovenia"},{"name":"Jo\u017eef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2074-4678","authenticated-orcid":false,"given":"Marko","family":"Jordan","sequence":"additional","affiliation":[{"name":"Department of Intelligent Systems, Jo\u017eef Stefan Institute, 1000 Ljubljana, Slovenia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0770-4268","authenticated-orcid":false,"given":"Hristijan","family":"Gjoreski","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University, 1000 Skopje, North Macedonia"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3219-2935","authenticated-orcid":false,"given":"Mitja","family":"Lu\u0161trek","sequence":"additional","affiliation":[{"name":"Department of Intelligent Systems, Jo\u017eef Stefan Institute, 1000 Ljubljana, Slovenia"},{"name":"Jo\u017eef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,9]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2016). World Health Statistics\u2013Monitoring Health for the Sdgs, WHO."},{"key":"ref_2","unstructured":"TEAM, Lifestyles Statistics (2015). Health and Social Care Information Centre, Statistics on Obesity, Physical Activity and Diet."},{"key":"ref_3","unstructured":"GBD 2017 Diet Collaborators (2019). Health effects of dietary risks in 195 countries, 1990\u20132017: A systematic analysis for the Global Burden of Disease Study. 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