{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T15:18:59Z","timestamp":1785424739787,"version":"3.56.0"},"reference-count":47,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T00:00:00Z","timestamp":1783468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council","award":["NSTC 114-2622-E-A49-026-"],"award-info":[{"award-number":["NSTC 114-2622-E-A49-026-"]}]},{"award":["NSTC 114-2622-E-A49-026-"],"award-info":[{"award-number":["NSTC 114-2622-E-A49-026-"]}],"id":[{"id":"https:\/\/ror.org\/02kv4zf79","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gait analysis is used in the diagnosis, rehabilitation, and longitudinal monitoring of degenerative spinal disease (DSD). However, conventional gait assessment commonly depends on subjective visual observation or laboratory-based motion-capture systems, which restrict accessibility and routine clinical use. This study presents an edge-enabled real-time gait analysis framework for DSD using two ankle-worn inertial measurement units (IMUs). The proposed framework integrates causal gait-event detection with spatiotemporal gait estimation, including stride length, stride height, stride frequency, and swing ratio, across Regular-, Toe-, Heel-, and Tandem-Walk tasks. To improve the stability of wearable gait estimation, the framework incorporates a cycle-wise initial sensor-orientation correction strategy with inter-cycle horizontal velocity continuity, reducing reliance on conventional zero-velocity update (ZUPT) resetting. A percentile-referenced, separability-weighted composite score was also developed to combine average gait performance, step-to-step variability, and gait asymmetry into an interpretable clinical index. Algorithm validation was conducted using an optical motion-capture system as the reference standard. The proposed framework, however, is intended for deployment using ankle-worn IMUs and edge-based computation without requiring optical cameras, reflective markers, or dedicated motion-capture laboratories during routine operation. Experimental results showed centimeter-level spatial estimation accuracy. The composite scoring framework achieved accuracy\/F1-scores of 0.971\/0.981 for Regular-Walk, 0.934\/0.956 for Toe-Walk, 0.955\/0.970 for Heel-Walk, and 0.876\/0.919 for Tandem-Walk. Feature analysis indicated that stride length, stride frequency, swing ratio, and step-to-step variability provided the greatest discrimination between healthy controls and spinal patients, with stronger group separation observed in Regular-, Toe-, and Heel-Walk tasks. These results suggest that ankle-mounted IMU sensing combined with lightweight edge-based computation and interpretable gait scoring may provide a practical approach for point-of-care gait assessment and remote functional monitoring in DSD. The proposed system operates using only two ankle-mounted IMUs during routine deployment, while optical motion capture was employed exclusively as a laboratory reference for validation.<\/jats:p>","DOI":"10.3390\/s26144339","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:16:18Z","timestamp":1783527378000},"page":"4339","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Edge-Enabled Real-Time Gait Assessment for Degenerative Spinal Disease Using Wearable Inertial Sensors"],"prefix":"10.3390","volume":"26","author":[{"given":"Kuei-Ann","family":"Wen","sequence":"first","affiliation":[{"name":"Department of Electronics Engineering, National Yang Ming Chiao Tung University, Hsinchu City 30010, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li-Hsieh","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, National Yang Ming Chiao Tung University, Hsinchu City 30010, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5270-2878","authenticated-orcid":false,"given":"Jiun-Lin","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung City 20401, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen-Nen","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Chang Gung Memorial Hospital, Taoyuan City 333423, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Shih","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, National Yang Ming Chiao Tung University, Hsinchu City 30010, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s00508-016-1096-4","article-title":"Gait disorders in adults and the elderly: A clinical guide","volume":"129","author":"Pirker","year":"2017","journal-title":"Wien. 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