{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T05:10:55Z","timestamp":1773810655851,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,6]],"date-time":"2022-01-06T00:00:00Z","timestamp":1641427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson\u2019s disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability\/gait difficulty (PIGD) score in PD patients by monitoring gait through a single inertial measurement unit (IMU) and machine-learning algorithms. Methods: Thirty-one PD patients underwent a 7-m timed-up-and-go test while monitored through an IMU placed on the thigh, both under (ON) and not under (OFF) dopaminergic therapy. After pre-processing procedures and feature selection, a support vector regression model was implemented to predict PIGD scores and to investigate the impact of L-Dopa and freezing of gait (FOG) on regression models. Results: Specific time- and frequency-domain features correlated with PIGD scores. After optimizing the dimensionality reduction methods and the model parameters, regression algorithms demonstrated different performance in the PIGD prediction in patients OFF and ON therapy (r = 0.79 and 0.75 and RMSE = 0.19 and 0.20, respectively). Similarly, regression models showed different performances in the PIGD prediction, in patients with FOG, ON and OFF therapy (r = 0.71 and RMSE = 0.27; r = 0.83 and RMSE = 0.22, respectively) and in those without FOG, ON and OFF therapy (r = 0.85 and RMSE = 0.19; r = 0.79 and RMSE = 0.21, respectively). Conclusions: Optimized support vector regression models have high feasibility in predicting PIGD scores in PD. L-Dopa and FOG affect regression model performances. Overall, a single inertial sensor may help to remotely assess axial motor impairment in PD patients.<\/jats:p>","DOI":"10.3390\/s22020412","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"412","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Predicting Axial Impairment in Parkinson\u2019s Disease through a Single Inertial Sensor"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0875-6913","authenticated-orcid":false,"given":"Luigi","family":"Borz\u00ec","sequence":"first","affiliation":[{"name":"Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy"},{"name":"Polito<sup>BIO<\/sup>Med Lab\u2014Biomedical Engineering Laboratory, Politecnico di Torino, 10129 Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7338-9297","authenticated-orcid":false,"given":"Ivan","family":"Mazzetta","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, 00184 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0227-1993","authenticated-orcid":false,"given":"Alessandro","family":"Zampogna","sequence":"additional","affiliation":[{"name":"Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9903-5550","authenticated-orcid":false,"given":"Antonio","family":"Suppa","sequence":"additional","affiliation":[{"name":"Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy"},{"name":"IRCCS NEUROMED Institute, 86077 Pozzilli, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1831-6786","authenticated-orcid":false,"given":"Fernanda","family":"Irrera","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Electronics and Telecommunication, Sapienza University of Rome, 00184 Rome, Italy"}]},{"given":"Gabriella","family":"Olmo","sequence":"additional","affiliation":[{"name":"Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy"},{"name":"Polito<sup>BIO<\/sup>Med Lab\u2014Biomedical Engineering Laboratory, Politecnico di Torino, 10129 Turin, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,6]]},"reference":[{"key":"ref_1","first-page":"323","article-title":"Diagnosis and Treatment of Parkinson Disease: A Review","volume":"60","author":"Armstrong","year":"2020","journal-title":"JAMA"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1097\/WCO.0b013e328305bdaf","article-title":"Gait disorders and balance disturbances in Parkinson\u2019s disease: Clinical update and pathophysiology","volume":"21","author":"Boonstra","year":"2008","journal-title":"Curr. 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