{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T17:16:19Z","timestamp":1767374179947,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T00:00:00Z","timestamp":1575504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 research and innovation ITN program &quot;Keep Control&quot;","award":["721577"],"award-info":[{"award-number":["721577"]}]},{"name":"Parkinson\u2019s UK","award":["J-0802, G-1301"],"award-info":[{"award-number":["J-0802, G-1301"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Early diagnosis of Parkinson\u2019s diseases (PD) is challenging; applying machine learning (ML) models to gait characteristics may support the classification process. Comparing performance of ML models used in various studies can be problematic due to different walking protocols and gait assessment systems. The objective of this study was to compare the impact of walking protocols and gait assessment systems on the performance of a support vector machine (SVM) and random forest (RF) for classification of PD. 93 PD and 103 controls performed two walking protocols at their normal pace: (i) four times along a 10 m walkway (intermittent walk-IW), (ii) walking for 2 minutes on a 25 m oval circuit (continuous walk-CW). 14 gait characteristics were extracted from two different systems (an instrumented walkway\u2014GAITRite; and an accelerometer attached at the lower back\u2014Axivity). SVM and RF were trained on normalized data (accounting for step velocity, gender, age and BMI) and evaluated using 10-fold cross validation with area under the curve (AUC). Overall performance was higher for both systems during CW compared to IW. SVM performed better than RF. With SVM, during CW Axivity significantly outperformed GAITRite (AUC: 87.83 \u00b1 7.81% vs. 80.49 \u00b1 9.85%); during IW systems performed similarly. These findings suggest that choice of testing protocol and sensing system may have a direct impact on ML PD classification results and highlight the need for standardization for wide scale implementation.<\/jats:p>","DOI":"10.3390\/s19245363","type":"journal-article","created":{"date-parts":[[2019,12,5]],"date-time":"2019-12-05T11:16:23Z","timestamp":1575544583000},"page":"5363","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Comparison of Walking Protocols and Gait Assessment Systems for Machine Learning-Based Classification of Parkinson\u2019s Disease"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4885-7455","authenticated-orcid":false,"given":"Rana Zia Ur","family":"Rehman","sequence":"first","affiliation":[{"name":"Institute of Neuroscience\/Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1154-4751","authenticated-orcid":false,"given":"Silvia","family":"Del Din","sequence":"additional","affiliation":[{"name":"Institute of Neuroscience\/Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK"}]},{"given":"Jian Qing","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Mathematics, Statistics, and Physics, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK"}]},{"given":"Brook","family":"Galna","sequence":"additional","affiliation":[{"name":"Institute of Neuroscience\/Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK"},{"name":"School of Biomedical, Nutritional and Sport Sciences, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK"}]},{"given":"Sue","family":"Lord","sequence":"additional","affiliation":[{"name":"Institute of Neuroscience\/Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK"},{"name":"Department of Physiotherapy, Auckland University of Technology, Auckland 92006, New Zealand"}]},{"given":"Alison J.","family":"Yarnall","sequence":"additional","affiliation":[{"name":"Institute of Neuroscience\/Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK"},{"name":"The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK"}]},{"given":"Yu","family":"Guan","sequence":"additional","affiliation":[{"name":"School of Computing, Newcastle University, Newcastle Upon Tyne NE4 5TG, UK"}]},{"given":"Lynn","family":"Rochester","sequence":"additional","affiliation":[{"name":"Institute of Neuroscience\/Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK"},{"name":"The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1989","DOI":"10.1212\/WNL.0b013e318293e2ce","article-title":"The coming crisis: Obtaining care for the growing burden of neurodegenerative conditions","volume":"80","author":"Dorsey","year":"2013","journal-title":"Neurology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1136\/jnnp.2007.131045","article-title":"Parkinson\u2019s disease: Clinical features and diagnosis","volume":"79","author":"Jankovic","year":"2008","journal-title":"J. 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