{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T08:13:35Z","timestamp":1780560815038,"version":"3.54.1"},"reference-count":37,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T00:00:00Z","timestamp":1660780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","award":["NIH R01HD097786-01A1"],"award-info":[{"award-number":["NIH R01HD097786-01A1"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Sleep plays a critical role in stroke recovery. However, there are limited practices to measure sleep for individuals with stroke, thus inhibiting our ability to identify and treat poor sleep quality. Wireless, body-worn sensors offer a solution for continuous sleep monitoring. In this study, we explored the feasibility of (1) collecting overnight biophysical data from patients with subacute stroke using a simple sensor system and (2) constructing machine-learned algorithms to detect sleep stages. Ten individuals with stroke in an inpatient rehabilitation hospital wore two wireless sensors during a single night of sleep. Polysomnography served as ground truth to classify different sleep stages. A population model, trained on data from multiple patients and tested on data from a separate patient, performed poorly for this limited sample. Personal models trained on data from one patient and tested on separate data from the same patient demonstrated markedly improved performance over population models and research-grade wearable devices to detect sleep\/wake. Ultimately, the heterogeneity of biophysical signals after stroke may present a challenge in building generalizable population models. Personal models offer a provisional method to capture high-resolution sleep metrics from simple wearable sensors by leveraging a single night of polysomnography data.<\/jats:p>","DOI":"10.3390\/s22166190","type":"journal-article","created":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T23:28:41Z","timestamp":1660865321000},"page":"6190","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Sleep Monitoring during Acute Stroke Rehabilitation: Toward Automated Measurement Using Multimodal Wireless Sensors"],"prefix":"10.3390","volume":"22","author":[{"given":"Pin-Wei","family":"Chen","sequence":"first","affiliation":[{"name":"Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan Ability Lab, Chicago, IL 60611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8069-365X","authenticated-orcid":false,"given":"Megan K.","family":"O\u2019Brien","sequence":"additional","affiliation":[{"name":"Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan Ability Lab, Chicago, IL 60611, USA"},{"name":"Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL 60611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7111-4860","authenticated-orcid":false,"given":"Adam P.","family":"Horin","sequence":"additional","affiliation":[{"name":"Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan Ability Lab, Chicago, IL 60611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lori L.","family":"McGee Koch","sequence":"additional","affiliation":[{"name":"Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan Ability Lab, Chicago, IL 60611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1626-7669","authenticated-orcid":false,"given":"Jong Yoon","family":"Lee","sequence":"additional","affiliation":[{"name":"Sibel Health Inc., Niles, IL 60714, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuai","family":"Xu","sequence":"additional","affiliation":[{"name":"Sibel Health Inc., Niles, IL 60714, USA"},{"name":"Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phyllis C.","family":"Zee","sequence":"additional","affiliation":[{"name":"Center for Circadian and Sleep Medicine, Department of Neurology, Northwestern University, Chicago, IL 60611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vineet M.","family":"Arora","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Chicago Medicine, Chicago, IL 60637, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arun","family":"Jayaraman","sequence":"additional","affiliation":[{"name":"Max Nader Lab for Rehabilitation Technologies and Outcomes Research, Shirley Ryan Ability Lab, Chicago, IL 60611, USA"},{"name":"Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL 60611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1161\/STROKEAHA.113.003675","article-title":"Insomnia Subtypes and the Subsequent Risks of Stroke Report From a Nationally Representative Cohort","volume":"45","author":"Wu","year":"2014","journal-title":"Stroke"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1016\/B978-0-444-52007-4.00021-7","article-title":"Sleep and stroke","volume":"99","author":"Bassetti","year":"2011","journal-title":"Handb. 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