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EnglishIn this paper we investigated the use of smartphone sensors and Artificial Intelligence techniques for the automatic quantification of the MDS-UPDRS-Part III Leg Agility (LA) task, representative of lower limb bradykinesia. Methods: We collected inertial data from 93 PD subjects. Four expert neurologists provided clinical evaluations. We employed a novel Artificial Neural Network approach in order to get a continuous output, going beyond the MDS-UPDRS score discretization. Results: We found a Pearson correlation of 0.92 between algorithm output and average clinical score, compared to an inter-rater agreement index of 0.88. Furthermore, the classification error was less than 0.5 scale point in about 80% cases.Conclusions:Weproposedanobjectiveandreliabletoolfor theautomaticquantificationoftheMDS-UPDRSLegAgilitytask. In perspective, this tool is part of a larger monitoring program to be carried out during activities of daily living, and managed by the patients themselves.
Smartphone-based estimation of item 3.8 of the MDS-UPDRS-III for assessing leg agility in people with Parkinson’s disease” / Borzi', Luigi; Varrecchia, Marilena; Sibille, Stefano; Olmo, Gabriella; Alberto Artusi, Carlo; Fabbri, Margherita; Giorgio Rizzone, Mario; Romagnolo, Alberto; Zibetti, Maurizio; Lopiano, Leonardo. - In: IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY. - ISSN 2644-1276. - ELETTRONICO. - 1(2020), pp. 140-147.
| Titolo: | Smartphone-based estimation of item 3.8 of the MDS-UPDRS-III for assessing leg agility in people with Parkinson’s disease” |
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| Data di pubblicazione: | 2020 |
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| Digital Object Identifier (DOI): | http://dx.doi.org/10.1109/OJEMB.2020.2993463 |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| 09090332.pdf | 2a Post-print versione editoriale / Version of Record |  | Visibile a tuttiVisualizza/Apri |
http://hdl.handle.net/11583/2831832
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