{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T16:15:04Z","timestamp":1778688904900,"version":"3.51.4"},"reference-count":57,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The freezing of gait (FoG) presents a sudden challenge in sustaining movement which becomes a common gait issue in people with later stages of Parkinson\u2019s disease (PD). FoG often results in falls that reduces the individual\u2019s impact on life. A highly precise detection technique is required for accurate detection of FoG episodes automatically. This paper utilizes multivariate signal decomposition techniques, including Variational Mode Decomposition (VMD), Multivariate Variational Mode Decomposition (MVMD), and Successive Variational Mode Decomposition (SVMD). These techniques are utilized to extract time-frequency domain features from FoG signals. The Daphnet FoG dataset is used to evaluate performance in the studies. The features derived from the decomposition techniques serve as inputs to classifiers. In this study, five classifiers are employed including both machine learning and deep learning methods. The study attained the highest classification accuracy of 96.74\u202f% with the use of a 1D CNN. The proposed approach demonstrates the potential for advancing the automated detection and facilitating early-stage diagnosis and intervention in Freezing of gait.<\/jats:p>","DOI":"10.1515\/comp-2025-0048","type":"journal-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T16:02:36Z","timestamp":1778688156000},"source":"Crossref","is-referenced-by-count":0,"title":["A customised 1D-CNN for\u00a0recognition of\u00a0freezing of\u00a0gait in\u00a0Parkinson\u2019s disease using multivariate decomposition techniques"],"prefix":"10.1515","volume":"16","author":[{"given":"Nancy","family":"Rajendran","sequence":"first","affiliation":[{"name":"Department of Robotics Engineering , Karunya University , Coimbatore , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas G.","family":"Selvaraj","sequence":"additional","affiliation":[{"name":"Department of Bio Medical Engineering , Karunya University , Coimbatore , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kalkeseetharaman","family":"Pennagaram Krushnakumar","sequence":"additional","affiliation":[{"name":"Department of Bio Medical Engineering , Karunya University , Coimbatore , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ravi T.","family":"Ashwala","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology , Karunya University , Coimbatore , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Subathra","family":"Muthu Sinnasamy Pandian","sequence":"additional","affiliation":[{"name":"Department of Robotics Engineering , Karunya University , Coimbatore , India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"key":"2026051316023422748_j_comp-2025-0048_ref_004","doi-asserted-by":"crossref","unstructured":"S. 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