{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T11:42:20Z","timestamp":1784288540781,"version":"3.55.0"},"reference-count":40,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T00:00:00Z","timestamp":1670025600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"Ataxia-Telangiectasia Children\u2019s Project, NIH","doi-asserted-by":"publisher","award":["R01NS117826"],"award-info":[{"award-number":["R01NS117826"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Biogen","award":["R01NS117826"],"award-info":[{"award-number":["R01NS117826"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wearable sensor data is relatively easily collected and provides direct measurements of movement that can be used to develop useful behavioral biomarkers. Sensitive and specific behavioral biomarkers for neurodegenerative diseases are critical to supporting early detection, drug development efforts, and targeted treatments. In this paper, we use autoregressive hidden Markov models and a time-frequency approach to create meaningful quantitative descriptions of behavioral characteristics of cerebellar ataxias from wearable inertial sensor data gathered during movement. We create a flexible and descriptive set of features derived from accelerometer and gyroscope data collected from wearable sensors worn while participants perform clinical assessment tasks, and use these data to estimate disease status and severity. A short period of data collection (&lt;5 min) yields enough information to effectively separate patients with ataxia from healthy controls with very high accuracy, to separate ataxia from other neurodegenerative diseases such as Parkinson\u2019s disease, and to provide estimates of disease severity.<\/jats:p>","DOI":"10.3390\/s22239454","type":"journal-article","created":{"date-parts":[[2022,12,5]],"date-time":"2022-12-05T08:10:57Z","timestamp":1670227857000},"page":"9454","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Assessing Cerebellar Disorders with Wearable Inertial Sensor Data Using Time-Frequency and Autoregressive Hidden Markov Model Approaches"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1312-6473","authenticated-orcid":false,"given":"Karin C.","family":"Knudson","sequence":"first","affiliation":[{"name":"Data Intensive Studies Center, Tufts University, Medford, MA 02155, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8741-0621","authenticated-orcid":false,"given":"Anoopum S.","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1159\/000358801","article-title":"The global epidemiology of hereditary ataxia and spastic paraplegia: A systematic review of prevalence studies","volume":"42","author":"Ruano","year":"2014","journal-title":"Neuroepidemiology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1002\/mds.25945","article-title":"The prevalence of Parkinson\u2019s disease: A systematic review and meta-analysis","volume":"29","author":"Pringsheim","year":"2014","journal-title":"Mov. 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