{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:39:21Z","timestamp":1760060361980,"version":"build-2065373602"},"reference-count":55,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,27]],"date-time":"2025-08-27T00:00:00Z","timestamp":1756252800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sobel-Duncan Border Health Research Award"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Stroke-related impairment remains a leading cause of long-term disability, limiting individuals\u2019 ability to perform daily activities. While wearable sensors offer scalable monitoring solutions during rehabilitation, they struggle to distinguish functional from non-functional movements, and manual annotation of sensor data is labor-intensive and prone to inconsistency. In this paper, we propose a novel framework that uses large language models (LLMs) to generate activity descriptions from video frames of therapy sessions. These descriptions are aligned with concurrently recorded accelerometer signals to create labeled training data. Through exploratory analysis, we demonstrate that accelerometer signals exhibit distinct temporal and statistical patterns corresponding to specific activities, supporting the feasibility of generating natural language narratives directly from sensor data. Our findings lay the foundation for future development of sensor-to-text models that can enable automated, non-intrusive, and scalable stroke rehabilitation monitoring without the need for manual or video-based annotation.<\/jats:p>","DOI":"10.3390\/bioengineering12090922","type":"journal-article","created":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T10:29:18Z","timestamp":1756376958000},"page":"922","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Toward Sensor-to-Text Generation: Leveraging LLM-Based Video Annotations for Stroke Therapy Monitoring"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4640-2926","authenticated-orcid":false,"given":"Mohammad Akidul","family":"Hoque","sequence":"first","affiliation":[{"name":"Department of Computer Science, The University of Texas at El Paso, El Paso, TX 79968, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shamim","family":"Ehsan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Texas at El Paso, El Paso, TX 79968, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5846-673X","authenticated-orcid":false,"given":"Anuradha","family":"Choudhury","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Texas at El Paso, El Paso, TX 79968, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4735-6114","authenticated-orcid":false,"given":"Peter","family":"Lum","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, The Catholic University of America, Washington, DC 20064, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9402-5799","authenticated-orcid":false,"given":"Monika","family":"Akbar","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Texas at El Paso, El Paso, TX 79968, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0190-6923","authenticated-orcid":false,"given":"Shashwati","family":"Geed","sequence":"additional","affiliation":[{"name":"Department of Physical Therapy and Movement Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9277-5283","authenticated-orcid":false,"given":"M. Shahriar","family":"Hossain","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Texas at El Paso, El Paso, TX 79968, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e364","DOI":"10.1161\/STR.0000000000000375","article-title":"2021 Guideline for the Prevention of Stroke in Patients with Stroke and Transient Ischemic Attack: A Guideline from the American Heart Association\/American Stroke Association","volume":"52","author":"Kleindorfer","year":"2021","journal-title":"Stroke"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e139","DOI":"10.1161\/CIR.0000000000000757","article-title":"Heart Disease and Stroke Statistics-2020 Update: A Report from the American Heart Association","volume":"141","author":"Virani","year":"2020","journal-title":"Circulation"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Geed, S., Feit, P., Edwards, D.F., and Dromerick, A.W. 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