In 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. [10.1109/OJEMB.2020.2993463]

Smartphone-based estimation of item 3.8 of the MDS-UPDRS-III for assessing leg agility in people with Parkinson’s disease”

Luigi Borzì;Marilena Varrecchia;Gabriella Olmo;
2020

Abstract

In 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2831832