{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T22:14:08Z","timestamp":1780352048611,"version":"3.54.1"},"reference-count":18,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,26]],"date-time":"2022-12-26T00:00:00Z","timestamp":1672012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Swedish Research Council"},{"name":"MobiFog"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSAN"],"abstract":"<jats:p>Remote health monitoring systems play an important role in the healthcare sector. Edge computing is a key enabler for realizing these systems, where it is required to collect big data while providing real-time guarantees. In this study, we focus on remote cuff-less blood pressure (BP) monitoring through electrocardiogram (ECG) as a case study to evaluate the benefits of edge computing and compression. First, we investigate the state-of-the-art algorithms for BP estimation and ECG compression. Second, we develop a system to measure the ECG, estimate the BP, and store the results in the cloud with three different configurations: (i) estimation in the edge, (ii) estimation in the cloud, and (iii) estimation in the cloud with compressed transmission. Third, we evaluate the three approaches in terms of application latency, transmitted data volume, and power usage. In experiments with batches of 64 ECG samples, the edge computing approach has reduced average application latency by 15%, average power usage by 19%, and total transmitted volume by 85%, confirming that edge computing improves system performance significantly. Compressed transmission proved to be an alternative when network bandwidth is limited and edge computing is impractical.<\/jats:p>","DOI":"10.3390\/jsan12010002","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T03:03:31Z","timestamp":1672110211000},"page":"2","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Evaluating Edge Computing and Compression for Remote Cuff-Less Blood Pressure Monitoring"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3297-0995","authenticated-orcid":false,"given":"Ward","family":"Goossens","sequence":"first","affiliation":[{"name":"Faculty of Applied Engineering, University of Antwerp, 2020 Antwerp, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9453-2838","authenticated-orcid":false,"given":"Dino","family":"Mustefa","sequence":"additional","affiliation":[{"name":"Embedded Systems, ALTEN Sweden AB, 118 46 Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8107-9640","authenticated-orcid":false,"given":"Detlef","family":"Scholle","sequence":"additional","affiliation":[{"name":"Embedded Systems, ALTEN Sweden AB, 118 46 Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5590-0784","authenticated-orcid":false,"given":"Hossein","family":"Fotouhi","sequence":"additional","affiliation":[{"name":"School of Innovation, Design, and Engineering, M\u00e4lardalen University, 722 20 V\u00e4ster\u00e5s, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4926-6737","authenticated-orcid":false,"given":"Joachim","family":"Denil","sequence":"additional","affiliation":[{"name":"Faculty of Applied Engineering, University of Antwerp, 2020 Antwerp, Belgium"},{"name":"Flanders Make Strategic Research Centre, 3920 Lommel, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,26]]},"reference":[{"key":"ref_1","unstructured":"Dey, N., Ashour, A.S., Bhatt, C., and James Fong, S. 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