{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T22:28:30Z","timestamp":1776119310931,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,13]],"date-time":"2024-07-13T00:00:00Z","timestamp":1720828800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000057","name":"NIGMS","doi-asserted-by":"publisher","award":["P20GM103499"],"award-info":[{"award-number":["P20GM103499"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"NIGMS","doi-asserted-by":"publisher","award":["P30 DK072476"],"award-info":[{"award-number":["P30 DK072476"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"NIGMS","doi-asserted-by":"publisher","award":["U54 GM104940"],"award-info":[{"award-number":["U54 GM104940"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"NIGMS","doi-asserted-by":"publisher","award":["UG3DA048510"],"award-info":[{"award-number":["UG3DA048510"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"NIGMS","doi-asserted-by":"publisher","award":["R01DA048094"],"award-info":[{"award-number":["R01DA048094"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011073","name":"NORC Center","doi-asserted-by":"publisher","award":["P20GM103499"],"award-info":[{"award-number":["P20GM103499"]}],"id":[{"id":"10.13039\/100011073","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011073","name":"NORC Center","doi-asserted-by":"publisher","award":["P30 DK072476"],"award-info":[{"award-number":["P30 DK072476"]}],"id":[{"id":"10.13039\/100011073","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011073","name":"NORC Center","doi-asserted-by":"publisher","award":["U54 GM104940"],"award-info":[{"award-number":["U54 GM104940"]}],"id":[{"id":"10.13039\/100011073","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011073","name":"NORC Center","doi-asserted-by":"publisher","award":["UG3DA048510"],"award-info":[{"award-number":["UG3DA048510"]}],"id":[{"id":"10.13039\/100011073","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011073","name":"NORC Center","doi-asserted-by":"publisher","award":["R01DA048094"],"award-info":[{"award-number":["R01DA048094"]}],"id":[{"id":"10.13039\/100011073","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["P20GM103499"],"award-info":[{"award-number":["P20GM103499"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["P30 DK072476"],"award-info":[{"award-number":["P30 DK072476"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["U54 GM104940"],"award-info":[{"award-number":["U54 GM104940"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["UG3DA048510"],"award-info":[{"award-number":["UG3DA048510"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R01DA048094"],"award-info":[{"award-number":["R01DA048094"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH\/NIDA","doi-asserted-by":"publisher","award":["P20GM103499"],"award-info":[{"award-number":["P20GM103499"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH\/NIDA","doi-asserted-by":"publisher","award":["P30 DK072476"],"award-info":[{"award-number":["P30 DK072476"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH\/NIDA","doi-asserted-by":"publisher","award":["U54 GM104940"],"award-info":[{"award-number":["U54 GM104940"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH\/NIDA","doi-asserted-by":"publisher","award":["UG3DA048510"],"award-info":[{"award-number":["UG3DA048510"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH\/NIDA","doi-asserted-by":"publisher","award":["R01DA048094"],"award-info":[{"award-number":["R01DA048094"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The characterization of human behavior in real-world contexts is critical for developing a comprehensive model of human health. Recent technological advancements have enabled wearables and sensors to passively and unobtrusively record and presumably quantify human behavior. Better understanding human activities in unobtrusive and passive ways is an indispensable tool in understanding the relationship between behavioral determinants of health and diseases. Adult individuals (N = 60) emulated the behaviors of smoking, exercising, eating, and medication (pill) taking in a laboratory setting while equipped with smartwatches that captured accelerometer data. The collected data underwent expert annotation and was used to train a deep neural network integrating convolutional and long short-term memory architectures to effectively segment time series into discrete activities. An average macro-F1 score of at least 85.1 resulted from a rigorous leave-one-subject-out cross-validation procedure conducted across participants. The score indicates the method\u2019s high performance and potential for real-world applications, such as identifying health behaviors and informing strategies to influence health. Collectively, we demonstrated the potential of AI and its contributing role to healthcare during the early phases of diagnosis, prognosis, and\/or intervention. From predictive analytics to personalized treatment plans, AI has the potential to assist healthcare professionals in making informed decisions, leading to more efficient and tailored patient care.<\/jats:p>","DOI":"10.3390\/s24144542","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T14:15:49Z","timestamp":1721052949000},"page":"4542","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Toward Concurrent Identification of Human Activities with a Single Unifying Neural Network Classification: First Step"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3385-4526","authenticated-orcid":false,"given":"Andrew","family":"Smith","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Musa","family":"Azeem","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chrisogonas O.","family":"Odhiambo","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3889-3636","authenticated-orcid":false,"given":"Pamela J.","family":"Wright","sequence":"additional","affiliation":[{"name":"Advancing Chronic Care Outcomes through Research and iNnovation (ACORN) Center, Department of Biobehavioral Health & Nursing Science, College of Nursing, University of South Carolina, Columbia, SC 29208, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4603-3719","authenticated-orcid":false,"given":"Hanim E.","family":"Diktas","sequence":"additional","affiliation":[{"name":"Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA 70808, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Spencer","family":"Upton","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Psychological Sciences, and Cognitive Neuroscience Systems Core Facility, University of Missouri, Columbia, MO 65211, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8125-4015","authenticated-orcid":false,"given":"Corby K.","family":"Martin","sequence":"additional","affiliation":[{"name":"Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, LA 70808, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brett","family":"Froeliger","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Psychological Sciences, and Cognitive Neuroscience Systems Core Facility, University of Missouri, Columbia, MO 65211, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2706-2116","authenticated-orcid":false,"given":"Cynthia F.","family":"Corbett","sequence":"additional","affiliation":[{"name":"Advancing Chronic Care Outcomes through Research and iNnovation (ACORN) Center, Department of Biobehavioral Health & Nursing Science, College of Nursing, University of South Carolina, Columbia, SC 29208, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1581-3464","authenticated-orcid":false,"given":"Homayoun","family":"Valafar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,13]]},"reference":[{"key":"ref_1","unstructured":"CDC (2024, June 03). Office on Smoking and Health (OSH)\u2014cdc.gov, Available online: https:\/\/www.cdc.gov\/tobacco\/programs\/index.html."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.nutres.2018.06.002","article-title":"The importance of healthy dietary patterns in chronic disease prevention","volume":"70","author":"Neuhouser","year":"2019","journal-title":"Nutr. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.3945\/jn.116.242552","article-title":"Dietary Patterns and Type 2 Diabetes: A Systematic Literature Review and Meta-Analysis of Prospective Studies","volume":"147","author":"Jannasch","year":"2017","journal-title":"J. Nutr."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ravelli, M.N., and Schoeller, D.A. (2020). Traditional Self-Reported Dietary Instruments Are Prone to Inaccuracies and New Approaches Are Needed. Front. Nutr., 7.","DOI":"10.3389\/fnut.2020.00090"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.3945\/jn.115.219634","article-title":"Addressing Current Criticism Regarding the Value of Self-Report Dietary Data","volume":"145","author":"Subar","year":"2015","journal-title":"J. Nutr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"20552076231158314","DOI":"10.1177\/20552076231158314","article-title":"Rationale and design of the SenseWhy project: A passive sensing and ecological momentary assessment study on characteristics of overeating episodes","volume":"9","author":"Alshurafa","year":"2023","journal-title":"Digit. Health"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e20625","DOI":"10.2196\/20625","article-title":"A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study","volume":"8","author":"Morshed","year":"2020","journal-title":"JMIR mHealth uHealth"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1111\/dom.14737","article-title":"Smartwatch gesture-based meal reminders improve glycaemic control","volume":"24","author":"Corbett","year":"2022","journal-title":"Diabetes Obes. Metab."},{"key":"ref_9","unstructured":"Odhiambo, C.O., Saha, S., Martin, C.K., and Valafar, H. (2022). Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, S., Alharbi, R., Stogin, W., Pourhomayun, M., Spring, B., and Alshurafa, N. (2016, January 15\u201316). Food watch: Detecting and characterizing eating episodes through feeding gestures. Proceedings of the 11th EAI International Conference on Body Area Networks. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), BodyNets \u201916, Turin, Italy.","DOI":"10.4108\/eai.15-12-2016.2267793"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107176","DOI":"10.1016\/j.appet.2023.107176","article-title":"An explanation for the accuracy of sensor-based measures of energy intake: Amount of food consumed matters more than dietary composition","volume":"194","author":"Chou","year":"2024","journal-title":"Appetite"},{"key":"ref_12","unstructured":"Cohen, R.A., and Adams, P.F. (2024, June 13). Use of the Internet for Health Information: United States, 2009. Available online: https:\/\/www.researchgate.net\/profile\/Robin-Cohen-3\/publication\/51854065_Use_of_the_internet_for_health_information_United_States_2009\/links\/616061ac0bf51d481755bbe3\/Use-of-the-internet-for-health-information-United-States-2009.pdf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lam, W.Y., and Fresco, P. (2015). Medication adherence measures: An overview. BioMed Res. Int., 2015.","DOI":"10.1155\/2015\/217047"},{"key":"ref_14","unstructured":"Sabat\u00e9, E. (2003). Adherence to Long-Term Therapies: Evidence for Action, World Health Organization."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kvarnstr\u00f6m, K., Westerholm, A., Airaksinen, M., and Liira, H. (2021). Factors contributing to medication adherence in patients with a chronic condition: A scoping review of qualitative research. Pharmaceutics, 13.","DOI":"10.3390\/pharmaceutics13071100"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e2314211","DOI":"10.1001\/jamanetworkopen.2023.14211","article-title":"Cost-related medication nonadherence and desire for medication cost information among adults aged 65 years and older in the US in 2022","volume":"6","author":"Dusetzina","year":"2023","journal-title":"JAMA Netw. Open"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.ypmed.2017.03.008","article-title":"Medication adherence outcomes of 771 intervention trials: Systematic review and meta-analysis","volume":"99","author":"Conn","year":"2017","journal-title":"Prev. Med."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Aldeer, M., Javanmard, M., and Martin, R.P. (2018). A review of medication adherence monitoring technologies. Appl. Syst. Innov., 1.","DOI":"10.3390\/asi1020014"},{"key":"ref_19","first-page":"117","article-title":"An overview of the common methods used to measure treatment adherence","volume":"92","author":"Anghel","year":"2019","journal-title":"Med. Pharm. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.jsat.2015.05.002","article-title":"Concordance of direct and indirect measures of medication adherence in a treatment trial for cannabis dependence","volume":"57","author":"Baker","year":"2015","journal-title":"J. Subst. Abus. Treat."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., and Alshurafa, N. (2022). Deep learning in human activity recognition with wearable sensors: A review on advances. Sensors, 22.","DOI":"10.3390\/s22041476"},{"key":"ref_22","unstructured":"Lee, S.M., Yoon, S.M., and Cho, H. (2017, January 13\u201316). Human activity recognition from accelerometer data using Convolutional Neural Network. Proceedings of the 2017 IEEE International Conference on Big Data and Smart Computing (Bigcomp), Jeju, Republic of Korea."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"19361","DOI":"10.1007\/s11042-020-10435-1","article-title":"A time-efficient convolutional neural network model in human activity recognition","volume":"80","author":"Gholamrezaii","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"51","DOI":"10.37965\/jait.2020.0051","article-title":"Human activity recognition and embedded application based on convolutional neural network","volume":"1","author":"Xu","year":"2021","journal-title":"J. Artif. Intell. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, Y., and Xue, Y. (2015, January 9\u201312). A deep learning approach to human activity recognition based on single accelerometer. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong, China.","DOI":"10.1109\/SMC.2015.263"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e42714","DOI":"10.2196\/42714","article-title":"Detecting Medication-Taking Gestures Using Machine Learning and Accelerometer Data Collected via Smartwatch Technology: Instrument Validation Study","volume":"10","author":"Odhiambo","year":"2023","journal-title":"JMIR Hum. Factors"},{"key":"ref_27","unstructured":"Cole, C.A., Janos, B., Anshari, D., Thrasher, J.F., Strayer, S., and Valafar, H. (2020). Recognition of smoking gesture using smart watch technology. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e20464","DOI":"10.2196\/20464","article-title":"Quantification of smoking characteristics using smartwatch technology: Pilot feasibility study of new technology","volume":"5","author":"Cole","year":"2021","journal-title":"JMIR Form. Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Smith, A., Anand, H., Milosavljevic, S., Rentschler, K.M., Pocivavsek, A., and Valafar, H. (2021, January 15\u201317). Application of machine learning to sleep stage classification. Proceedings of the 2021 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.","DOI":"10.1109\/CSCI54926.2021.00130"},{"key":"ref_30","unstructured":"Chien, H.Y.S., Goh, H., Sandino, C.M., and Cheng, J.Y. (2022). Maeeg: Masked auto-encoder for eeg representation learning. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, M., Scronce, G., Finetto, C., Coupland, K., Zhong, M., Lambert, M.E., Baker, A., Luo, F., and Seo, N.J. (2023). Application of Deep Learning Algorithm to Monitor Upper Extremity Task Practice. Sensors, 23.","DOI":"10.3390\/s23136110"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1038\/s41398-023-02399-1","article-title":"Kynurenine aminotransferase II inhibition promotes sleep and rescues impairments induced by neurodevelopmental insult","volume":"13","author":"Milosavljevic","year":"2023","journal-title":"Transl. Psychiatry"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yan, W., and Oates, T. (2017, January 14\u201319). Time series classification from scratch with deep neural networks: A strong baseline. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966039"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e9035","DOI":"10.2196\/mhealth.9035","article-title":"Detecting smoking events using accelerometer data collected via smartwatch technology: Validation study","volume":"5","author":"Cole","year":"2017","journal-title":"JMIR mHealth uHealth"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Radosavovic, I., Kosaraju, R.P., Girshick, R., He, K., and Doll\u00e1r, P. (2020, January 13\u201319). Designing network design spaces. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01044"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_38","unstructured":"Loshchilov, I., and Hutter, F. (2017). Decoupled weight decay regularization. arXiv."},{"key":"ref_39","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_40","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst., 32, Available online: https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/bdbca288fee7f92f2bfa9f7012727740-Abstract.html."},{"key":"ref_41","unstructured":"Baum, E., and Wilczek, F. (2024, June 13). Supervised Learning of Probability Distributions by Neural Networks. In Proceedings of the Neural Information Processing Systems, 1987. Available online: https:\/\/papers.baulab.info\/papers\/also\/Baum-1988.pdf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_43","unstructured":"LeCun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., and Jackel, L. (1989). Handwritten digit recognition with a back-propagation network. Adv. Neural Inf. Process. Syst., 2, Available online: https:\/\/proceedings.neurips.cc\/paper\/1989\/hash\/53c3bce66e43be4f209556518c2fcb54-Abstract.html."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Goutte, C., and Gaussier, E. (2005). A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. European Conference on Information Retrieval, Springer.","DOI":"10.1007\/978-3-540-31865-1_25"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e106","DOI":"10.2196\/jmir.8701","article-title":"Just-in-Time Feedback in Diet and Physical Activity Interventions: Systematic Review and Practical Design Framework","volume":"20","author":"Schembre","year":"2018","journal-title":"J. Med. Internet Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2358","DOI":"10.1038\/s41366-020-00693-2","article-title":"Review of the validity and feasibility of image-assisted methods for dietary assessment","volume":"44","author":"Martin","year":"2020","journal-title":"Int. J. Obes."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/jcpt.12757","article-title":"Outcome measures for adherence data from a medication event monitoring system: A literature review","volume":"44","author":"Hartman","year":"2019","journal-title":"J. Clin. Pharm. Ther."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/14\/4542\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:16:18Z","timestamp":1760109378000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/14\/4542"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,13]]},"references-count":48,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["s24144542"],"URL":"https:\/\/doi.org\/10.3390\/s24144542","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,13]]}}}