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Fine\u2010tuning of clinical diagnostic criteria over the past few decades, as well as device\u2010based qualitative analysis, has resulted in incremental improvements to diagnostic accuracy. Accelerometric assessments are commonplace, enabling clinicians to capture high\u2010resolution oscillatory properties of tremor, which recently have been the focus of various machine\u2010learning (ML) studies. In this context, the application of ML models to accelerometric recordings provides the potential for less\u2010biased classification and quantification of tremor disorders. However, if implemented incorrectly, ML can result in spurious or nongeneralizable results and misguided conclusions. This work summarizes and highlights recent developments in ML tools for tremor research, with a focus on supervised ML. We aim to highlight the opportunities and limitations of such approaches and provide future directions while simultaneously guiding the reader through the process of applying ML to analyze tremor data. We identify the need for the movement disorder community to take a more proactive role in the application of these novel analytical technologies, which so far have been predominantly pursued by the engineering and data analysis field. Ultimately, big\u2010data approaches offer the possibility to identify generalizable patterns but warrant meaningful translation into clinical practice. \u00a9 2023 The Authors.\n                    <jats:italic>Movement Disorders<\/jats:italic>\n                    published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.\n                  <\/jats:p>","DOI":"10.1002\/mds.29376","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T01:50:28Z","timestamp":1679622628000},"page":"717-731","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Machine Learning in Tremor Analysis: Critique and Directions"],"prefix":"10.1002","volume":"38","author":[{"given":"Anwesan","family":"De","sequence":"first","affiliation":[{"name":"Department of Neurology University Hospital Wuerzburg  Wuerzburg Germany"},{"name":"Department of Electronics and 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