{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T17:30:49Z","timestamp":1778088649713,"version":"3.51.4"},"reference-count":27,"publisher":"Walter de Gruyter GmbH","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The process of diagnosing tremor patients often leads to misdiagnoses. Therefore, existing technical methods for analysing tremor are needed to more effectively distinguish between different diseases. For this purpose, a system has been developed that classifies measured tremor signals in real time. To achieve this, the hand tremor of 561 subjects has been measured in different hand positions. Acceleration and surface electromyography are recorded during the examination. For this study, data from subjects with Parkinson\u2019s Disease, Essential Tremor, and physiological tremor are considered. In a first signal analysis feature extraction is performed, and the resulting features are examined for their discriminative value. In a second step, three classification models based on different pattern recognition techniques are developed to classify the subjects with respect to their tremor type. With a trained decision tree, the three tremor types can be classified with a relative diagnostic accuracy of 83.14%. A neural network achieves 84.24% and the combination of both classifiers yields a relative diagnostic accuracy of 85.76%. The approach is promising and involving more features of the recorded time series will improve the discriminative value.<\/jats:p>","DOI":"10.1515\/bmt-2021-0140","type":"journal-article","created":{"date-parts":[[2022,2,26]],"date-time":"2022-02-26T17:44:56Z","timestamp":1645897496000},"page":"119-130","source":"Crossref","is-referenced-by-count":5,"title":["Real-time classification of movement patterns of tremor patients"],"prefix":"10.1515","volume":"67","author":[{"given":"Patricia","family":"Piepjohn","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Institute of Electrical and Information Engineering, Digital Signal Processing and System Theory, Kiel University , Kiel , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christin","family":"Bald","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Institute of Electrical and Information Engineering, Digital Signal Processing and System Theory, Kiel University , Kiel , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregor","family":"Kuhlenb\u00e4umer","sequence":"additional","affiliation":[{"name":"Department of Neurology , University Hospital Schleswig-Holstein , Kiel , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos Steffen","family":"Becktepe","sequence":"additional","affiliation":[{"name":"Department of Neurology , University Hospital Schleswig-Holstein , Kiel , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"G\u00fcnther","family":"Deuschl","sequence":"additional","affiliation":[{"name":"Department of Neurology , University Hospital Schleswig-Holstein , Kiel , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gerhard","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Institute of Electrical and Information Engineering, Digital Signal Processing and System Theory, Kiel University , Kiel , Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"2023033113202893622_j_bmt-2021-0140_ref_001","doi-asserted-by":"crossref","unstructured":"Deuschl, G, Bain, P, Brin, M. 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