{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T19:18:44Z","timestamp":1777058324016,"version":"3.51.4"},"reference-count":128,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,11]],"date-time":"2021-01-11T00:00:00Z","timestamp":1610323200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Westlake University","award":["041030080118"],"award-info":[{"award-number":["041030080118"]}]},{"name":"Bright Dream Joint Institute for Intelligent Robotics","award":["10318H991901"],"award-info":[{"award-number":["10318H991901"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>We review in this paper the wearable-based technologies intended for real-time monitoring of stroke-related physiological parameters. These measurements are undertaken to prevent death and disability due to stroke. We compare the various characteristics, such as weight, accessibility, frequency of use, data continuity, and response time of these wearables. It was found that the most user-friendly wearables can have limitations in reporting high-precision prediction outcomes. Therefore, we report also the trend of integrating these wearables into the internet of things (IoT) and combining electronic health records (EHRs) and machine learning (ML) algorithms to establish a stroke risk prediction system. Due to different characteristics, such as accessibility, time, and spatial resolution of various wearable-based technologies, strategies of applying different types of wearables to maximize the efficacy of stroke risk prediction are also reported. In addition, based on the various applications of multimodal electroencephalography\u2013functional near-infrared spectroscopy (EEG\u2013fNIRS) on stroke patients, the perspective of using this technique to improve the prediction performance is elaborated. Expected prediction has to be dynamically delivered with high-precision outcomes. There is a need for stroke risk stratification and management to reduce the resulting social and economic burden.<\/jats:p>","DOI":"10.3390\/s21020460","type":"journal-article","created":{"date-parts":[[2021,1,11]],"date-time":"2021-01-11T11:36:11Z","timestamp":1610364971000},"page":"460","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["Trends and Challenges of Wearable Multimodal Technologies for Stroke Risk Prediction"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3431-1806","authenticated-orcid":false,"given":"Yun-Hsuan","family":"Chen","sequence":"first","affiliation":[{"name":"CenBRAIN Lab., School of Engineering, Westlake University, Hangzhou 310024, China"},{"name":"Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou 310024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4137-7272","authenticated-orcid":false,"given":"Mohamad","family":"Sawan","sequence":"additional","affiliation":[{"name":"CenBRAIN Lab., School of Engineering, Westlake University, Hangzhou 310024, China"},{"name":"Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou 310024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1016\/S0140-6736(20)30925-9","article-title":"Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u20132019: A systematic analysis for the Global Burden of Disease Study 2019","volume":"396","author":"Vos","year":"2020","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/S1474-4422(19)30034-1","article-title":"Global, regional, and national burden of stroke, 1990\u20132016: A systematic analysis for the Global Burden of Disease Study 2016","volume":"18","author":"Johnson","year":"2019","journal-title":"Lancet Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/S1474-4422(18)30500-3","article-title":"Stroke in China: Advances and challenges in epidemiology, prevention, and management","volume":"18","author":"Wu","year":"2019","journal-title":"Lancet Neurol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"E56","DOI":"10.1161\/CIR.0000000000000659","article-title":"Heart Disease and Stroke Statistics\u20142019 Update A Report from the American Heart Association","volume":"139","author":"Benjamin","year":"2019","journal-title":"Circulation"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.annemergmed.2014.03.004","article-title":"Management of Hypertension in Stroke","volume":"64","author":"Miller","year":"2014","journal-title":"Ann. 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