{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T17:26:37Z","timestamp":1790443597350,"version":"4.1.0"},"reference-count":142,"publisher":"Walter de Gruyter GmbH","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Wearable home-monitoring devices acquiring various biosignals such as the electrocardiogram, photoplethysmogram, electromyogram, respirational activity and movements have become popular in many fields of research, medical diagnostics and commercial applications. Especially ambulatory settings introduce still unsolved challenges to the development of sensor hardware and smart signal processing approaches. This work gives a detailed insight into a novel wireless body sensor network and addresses critical aspects such as signal quality, synchronicity among multiple devices as well as the system\u2019s overall capabilities and limitations in cardiovascular monitoring. An early sign of typical cardiovascular diseases is often shown by disturbed autonomic regulations such as orthostatic intolerance. In that context, blood pressure measurements play an important role to observe abnormalities like hypo- or hypertensions. Non-invasive and unobtrusive blood pressure monitoring still poses a significant challenge, promoting alternative approaches including pulse wave velocity considerations. In the scope of this work, the presented hardware is applied to demonstrate the continuous extraction of multi modal parameters like pulse arrival time within a preliminary clinical study. A Schellong test to diagnose orthostatic hypotension which is typically based on blood pressure cuff measurements has been conducted, serving as an application that might significantly benefit from novel multi-modal measurement principles. It is further shown that the system\u2019s synchronicity is as precise as 30 \u03bcs and that the integrated analog preprocessing circuits and additional accelerometer data provide significant advantages in ambulatory measurement environments.<\/jats:p>","DOI":"10.1515\/bmt-2014-0178","type":"journal-article","created":{"date-parts":[[2015,10,20]],"date-time":"2015-10-20T10:18:59Z","timestamp":1445336339000},"page":"57-68","source":"Crossref","is-referenced-by-count":11,"title":["Multi-modal signal acquisition using a synchronized wireless body sensor network in geriatric patients"],"prefix":"10.1515","volume":"61","author":[{"given":"Maik","family":"Pflugradt","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steffen","family":"Mann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timo","family":"Tigges","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias","family":"G\u00f6rnig","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reinhold","family":"Orglmeister","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","reference":[{"key":"ref401","doi-asserted-by":"crossref","first-page":"1658","DOI":"10.1109\/SURV.2013.121313.00064","article-title":"Wireless body area networks a survey Tutorials","volume":"16","author":"Movassaghi","year":"2014","journal-title":"IEEE Commun 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