{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:52:37Z","timestamp":1779382357536,"version":"3.53.1"},"reference-count":22,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chengdu Science and Technology Bureau","award":["2021-YF05\u201302175-SN"],"award-info":[{"award-number":["2021-YF05\u201302175-SN"]}]},{"name":"Chengdu Science and Technology Bureau","award":["2023YFG0271"],"award-info":[{"award-number":["2023YFG0271"]}]},{"DOI":"10.13039\/501100004829","name":"Science and Technology Department of Sichuan Province","doi-asserted-by":"publisher","award":["2021-YF05\u201302175-SN"],"award-info":[{"award-number":["2021-YF05\u201302175-SN"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004829","name":"Science and Technology Department of Sichuan Province","doi-asserted-by":"publisher","award":["2023YFG0271"],"award-info":[{"award-number":["2023YFG0271"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Accurately distinguishing hemiplegic gait from healthy gait is significant for alleviating clinicians\u2019 diagnostic workloads and enhancing rehabilitation efficiency. The center of pressure (CoP) trajectory extracted from pressure sensor arrays can be utilized for hemiplegic gait recognition. Existing research studies on hemiplegic gait recognition based on plantar pressure have paid limited attention to the differences in recognition performance offered by CoP trajectories along different directions. To address this, this paper proposes a neural network model based on time\u2013frequency domain feature interaction\u2014the temporal\u2013frequency domain interaction network (TFDI-Net)\u2014to achieve efficient hemiplegic gait recognition. The work encompasses: (1) collecting CoP trajectory data using a pressure sensor array from 19 hemiplegic patients and 29 healthy subjects; (2) designing and implementing the TFDI-Net architecture, which extracts frequency domain features of the CoP trajectory via fast Fourier transform (FFT) and interacts or fuses them with time domain features to construct a discriminative joint representation; (3) conducting five-fold cross-validation comparisons with traditional machine learning methods and deep learning methods. Intra-fold data augmentation was performed by adding Gaussian noise to each training fold during partitioning. Box plots were employed to visualize and analyze the performance metrics of different models across test folds, revealing their stability and advantages. The results demonstrate that the proposed TFDI-Net outperforms traditional machine learning models, achieving improvements of 2.89% in recognition rate, 4.6% in F1-score, and 8.25% in recall.<\/jats:p>","DOI":"10.3390\/computers14080334","type":"journal-article","created":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T13:28:22Z","timestamp":1755523702000},"page":"334","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Time\u2013Frequency Feature Fusion Approach for Hemiplegic Gait Recognition"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9612-3272","authenticated-orcid":false,"given":"Linglong","family":"Mao","sequence":"first","affiliation":[{"name":"College of Computer Science and Cybersecurity, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanyong","family":"Mei","sequence":"additional","affiliation":[{"name":"College of Computer Science and Cybersecurity, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e14889","DOI":"10.1111\/cns.14889","article-title":"Identifying Biomarkers Related to Motor Function in Chronic Stroke: A fNIRS and TMS Study","volume":"30","author":"Cai","year":"2024","journal-title":"CNS Neurosci. 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