{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T23:31:43Z","timestamp":1783035103871,"version":"3.54.6"},"reference-count":54,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T00:00:00Z","timestamp":1770681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"U.S. National Science Foundation","doi-asserted-by":"publisher","award":["IIS-2205093"],"award-info":[{"award-number":["IIS-2205093"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Back pain is a pervasive issue affecting a significant portion of the population, often worsened by certain movements of the lower back. Assessing these movements is important for helping clinicians prescribe appropriate physical therapy. However, it can be difficult to monitor patients\u2019 movements remotely outside the clinic. High-fidelity data from motion capture sensors can be used to classify different movements, but these sensors are costly and impractical for use in free-living environments. Motion Tape (MT), a new fabric-based wearable sensor, addresses these issues by being low cost and portable. Despite these advantages, novelty and variability in sensor stability make the MT dataset small scale and inherent to noise. In this work, we propose the Motion-Tape Augmentation Inference Model (MT-AIM), a deep learning classification pipeline trained on MT data. In order to address the challenges of limited sample size and noise present within the MT dataset, MT-AIM leverages conditional generative models to generate synthetic MT data of a desired movement, as well as predicting joint kinematics as additional features. This combination of synthetic data generation and feature augmentation enables MT-AIM to achieve state-of-the-art accuracy in classifying lower back movements, bridging the gap between physiological sensing and movement analysis.<\/jats:p>","DOI":"10.3390\/s26041127","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T11:16:38Z","timestamp":1770722198000},"page":"1127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Assessing Low Back Movement with Motion Tape Sensor Data Through Deep Learning"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4782-5880","authenticated-orcid":false,"given":"Jared","family":"Levy","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2058-9299","authenticated-orcid":false,"given":"Aarti","family":"Lalwani","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8467-2984","authenticated-orcid":false,"given":"Elijah","family":"Wyckoff","sequence":"additional","affiliation":[{"name":"Active, Responsive, Multifunctional, and Ordered-Materials Research (ARMOR) Laboratory, Department of Structural Engineering, University of California San Diego, La Jolla, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1448-6251","authenticated-orcid":false,"given":"Kenneth J.","family":"Loh","sequence":"additional","affiliation":[{"name":"Active, Responsive, Multifunctional, and Ordered-Materials Research (ARMOR) Laboratory, Department of Structural Engineering, University of California San Diego, La Jolla, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8284-4789","authenticated-orcid":false,"given":"Sara P.","family":"Gombatto","sequence":"additional","affiliation":[{"name":"School of Physical Therapy, San Diego State University, San Diego, CA 92182, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8491-7937","authenticated-orcid":false,"given":"Rose","family":"Yu","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6485-0141","authenticated-orcid":false,"given":"Emilia","family":"Farcas","sequence":"additional","affiliation":[{"name":"Qualcomm Institute, University of California San Diego, La Jolla, CA 92093, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,10]]},"reference":[{"key":"ref_1","first-page":"49","article-title":"Global, regional and national burden of low back pain 1990\u20132019: A systematic analysis of the Global Burden of Disease study 2019","volume":"32","author":"Chen","year":"2022","journal-title":"J. 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