{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T01:49:21Z","timestamp":1767923361976,"version":"3.49.0"},"reference-count":18,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T00:00:00Z","timestamp":1695168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The year 2020 was definitely like no other [...]<\/jats:p>","DOI":"10.3390\/s23187993","type":"journal-article","created":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T22:38:45Z","timestamp":1695249525000},"page":"7993","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Special Issue: \u201cIntelligent Systems for Clinical Care and Remote Patient Monitoring\u201d"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7856-8761","authenticated-orcid":false,"given":"Giovanna","family":"Sannino","sequence":"first","affiliation":[{"name":"Institute for High-Performance Computing and Networking (ICAR), National Research Council, 80131 Naples, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9003-6194","authenticated-orcid":false,"given":"Antonio","family":"Celesti","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Sciences, Physical Sciences and Earth Sciences, University of Messina, 98122 Messina, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6127-1195","authenticated-orcid":false,"given":"Ivanoe","family":"De Falco","sequence":"additional","affiliation":[{"name":"Institute for High-Performance Computing and Networking (ICAR), National Research Council, 80131 Naples, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chew, K.T., Raman, V., and Then, P.H.H. (2021). Remote Arrhythmia Detection for Eldercare in Malaysia. Sensors, 21.","DOI":"10.3390\/s21248197"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pires, I.M., Denysyuk, H.V., Villasana, M.V., S\u00e1, J., Marques, D.L., Morgado, J.F., Albuquerque, C., and Zdravevski, E. (2022). Development technologies for the monitoring of six-minute walk test: A systematic review. Sensors, 22.","DOI":"10.3390\/s22020581"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mezzi, R., Yahyaoui, A., Krir, M.W., Boulila, W., and Koubaa, A. (2022). Mental health intent recognition for Arabic-speaking patients using the mini international neuropsychiatric interview (MINI) and BERT model. Sensors, 22.","DOI":"10.3390\/s22030846"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bernaldo de Quir\u00f3s, M., Douma, E., van den Akker-Scheek, I., Lamoth, C.J., and Maurits, N.M. (2022). Quantification of Movement in Stroke Patients under Free Living Conditions Using Wearable Sensors: A Systematic Review. Sensors, 22.","DOI":"10.3390\/s22031050"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Na, H., Park, S., and Dong, S.Y. (2022). Mixed reality-based interaction between human and virtual cat for mental stress management. Sensors, 22.","DOI":"10.3390\/s22031159"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Senk, S., Ulbricht, M., Tsokalo, I., Rischke, J., Li, S.C., Speidel, S., Nguyen, G.T., Seeling, P., and Fitzek, F.H. (2022). Healing hands: The tactile internet in future tele-healthcare. Sensors, 22.","DOI":"10.3390\/s22041404"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Makroum, M.A., Adda, M., Bouzouane, A., and Ibrahim, H. (2022). Machine learning and smart devices for diabetes management: Systematic review. Sensors, 22.","DOI":"10.3390\/s22051843"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chimamiwa, G., Giaretta, A., Alirezaie, M., Pecora, F., and Loutfi, A. (2022). Are Smart Homes Adequate for Older Adults with Dementia?. Sensors, 22.","DOI":"10.3390\/s22114254"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Mavrogiorgou, A., Kiourtis, A., Kleftakis, S., Mavrogiorgos, K., Zafeiropoulos, N., and Kyriazis, D. (2022). A Catalogue of Machine Learning Algorithms for Healthcare Risk Predictions. Sensors, 22.","DOI":"10.3390\/s22228615"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Prokopowicz, P., Miko\u0142ajewski, D., and Miko\u0142ajewska, E. (2022). Intelligent System for Detecting Deterioration of Life Satisfaction as Tool for Remote Mental-Health Monitoring. Sensors, 22.","DOI":"10.3390\/s22239214"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ubl, M., Koutny, T., Della Cioppa, A., De Falco, I., Tarantino, E., and Scafuri, U. (2022). Distributed Assessment of Virtual Insulin-Pump Settings Using SmartCGMS and DMMS. R for Diabetes Treatment. Sensors, 22.","DOI":"10.3390\/s22239445"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ferraris, C., Ronga, I., Pratola, R., Coppo, G., Bosso, T., Falco, S., Amprimo, G., Pettiti, G., Lo Priore, S., and Priano, L. (2022). Usability of the REHOME solution for the telerehabilitation in neurological diseases: Preliminary results on motor and cognitive platforms. Sensors, 22.","DOI":"10.3390\/s22239467"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Luo, Z., Ding, X., Hou, N., and Wan, J. (2022). A Deep-Learning-Based Collaborative Edge\u2013Cloud Telemedicine System for Retinopathy of Prematurity. Sensors, 23.","DOI":"10.3390\/s23010276"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Silvestri, S., Islam, S., Papastergiou, S., Tzagkarakis, C., and Ciampi, M. (2023). A Machine Learning Approach for the NLP-Based Analysis of Cyber Threats and Vulnerabilities of the Healthcare Ecosystem. Sensors, 23.","DOI":"10.3390\/s23020651"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Torres-Guzman, R.A., Paulson, M.R., Avila, F.R., Maita, K., Garcia, J.P., Forte, A.J., and Maniaci, M.J. (2023). Smartphones and threshold-based monitoring methods effectively detect falls remotely: A systematic review. Sensors, 23.","DOI":"10.3390\/s23031323"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Manouchehri, N., and Bouguila, N. (2023). Human Activity Recognition with an HMM-Based Generative Model. Sensors, 23.","DOI":"10.3390\/s23031390"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Brancato, V., Brancati, N., Esposito, G., La Rosa, M., Cavaliere, C., Allar\u00e0, C., Romeo, V., De Pietro, G., Salvatore, M., and Aiello, M. (2023). A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer. Sensors, 23.","DOI":"10.3390\/s23031552"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hassan, E., Elmougy, S., Ibraheem, M.R., Hossain, M.S., AlMutib, K., Ghoneim, A., AlQahtani, S.A., and Talaat, F.M. (2023). Enhanced Deep Learning Model for Classification of Retinal Optical Coherence Tomography Images. Sensors, 23.","DOI":"10.3390\/s23125393"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7993\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:53:56Z","timestamp":1760129636000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7993"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,20]]},"references-count":18,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23187993"],"URL":"https:\/\/doi.org\/10.3390\/s23187993","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,20]]}}}