{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T12:31:54Z","timestamp":1786365114170,"version":"build-2736575974"},"reference-count":43,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Parkinson\u2019s disease (PD) diagnosis and severity assessment increasingly rely on gait analysis. To capture complex gait dynamics from limited sensor data, we propose a novel multi-branch framework integrating a one-dimensional convolutional neural network (1D-CNN), a bidirectional gated recurrent unit (Bi-GRU), and Kolmogorov\u2013Arnold networks (KANs) (CNN-GRU-KAN) utilizing 18-channel vertical ground reaction force (VGRF) signals. Each module serves a distinct clinical purpose: the 1D-CNN branch with squeeze-and-excitation (SE) attention extracts localized spatial plantar pressure patterns, while the Bi-GRU branch with temporal attention captures long-range rhythm abnormalities. Crucially, the KAN serves as the classification head. By utilizing learnable B-spline functions instead of traditional fixed activations, KAN adaptively models the highly non-linear boundaries between healthy controls and varying PD severities, effectively mitigating overfitting. Under rigorous subject-independent cross-validation, our model achieves 98.43% accuracy for binary PD detection and 93.46% for five-class UPDRS severity grading. These results highlight the framework\u2019s strong potential for low-cost, unobtrusive clinical tracking and home monitoring.<\/jats:p>","DOI":"10.3390\/bioengineering13080905","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T11:38:09Z","timestamp":1786361889000},"page":"905","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CNN-GRU-KAN: A Novel Multi-Branch Framework for Parkinson\u2019s Disease Detection Based on Gait Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5226-3082","authenticated-orcid":false,"given":"Xingkai","family":"Fu","sequence":"first","affiliation":[{"name":"College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0736-2958","authenticated-orcid":false,"given":"Minlan","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6956-7641","authenticated-orcid":false,"given":"Mohammed A. A.","family":"Al-qaness","sequence":"additional","affiliation":[{"name":"College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China"},{"name":"College of Engineering and Information Technology, Emirates International University, Sana\u2019a 16881, Yemen"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"118221","DOI":"10.1016\/j.measurement.2025.118221","article-title":"Development of portable Raman spectrometer for Parkinson\u2019s disease diagnosis: A computational approach using salivary alpha-synuclein","volume":"256","author":"Garnaik","year":"2025","journal-title":"Measurement"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1109\/JBHI.2019.2903627","article-title":"Assessment of motor impairments in early untreated Parkinson\u2019s disease patients: The wearable electronics impact","volume":"24","author":"Ricci","year":"2019","journal-title":"IEEE J. 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