Smartphone-Based Estimation of Item 3.8 of the MDS-UPDRS-III for Assessing Leg Agility in People With Parkinson's Disease
<italic>Goal:</italic> 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. <italic>Methods:</italic> We...
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IEEE
2020-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
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| Online Access: | https://ieeexplore.ieee.org/document/9090332/ |
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| author | Luigi Borzi Marilena Varrecchia Stefano Sibille Gabriella Olmo Carlo Alberto Artusi Margherita Fabbri Mario Giorgio Rizzone Alberto Romagnolo Maurizio Zibetti Leonardo Lopiano |
| author_facet | Luigi Borzi Marilena Varrecchia Stefano Sibille Gabriella Olmo Carlo Alberto Artusi Margherita Fabbri Mario Giorgio Rizzone Alberto Romagnolo Maurizio Zibetti Leonardo Lopiano |
| author_sort | Luigi Borzi |
| collection | DOAJ |
| description | <italic>Goal:</italic> 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. <italic>Methods:</italic> 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. <italic>Results:</italic> 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. <italic>Conclusions:</italic> We proposed an objective and reliable tool for the automatic quantification of the MDS-UPDRS Leg Agility task. 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. |
| format | Article |
| id | doaj-art-9da799c70ed844d7b718d8785b85a7a9 |
| institution | Matheson Library |
| issn | 2644-1276 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Engineering in Medicine and Biology |
| spelling | doaj-art-9da799c70ed844d7b718d8785b85a7a92025-07-02T00:07:58ZengIEEEIEEE Open Journal of Engineering in Medicine and Biology2644-12762020-01-01114014710.1109/OJEMB.2020.29934639090332Smartphone-Based Estimation of Item 3.8 of the MDS-UPDRS-III for Assessing Leg Agility in People With Parkinson's DiseaseLuigi Borzi0https://orcid.org/0000-0003-0875-6913Marilena Varrecchia1Stefano Sibille2Gabriella Olmo3https://orcid.org/0000-0002-3670-9412Carlo Alberto Artusi4https://orcid.org/0000-0001-8579-3772Margherita Fabbri5Mario Giorgio Rizzone6Alberto Romagnolo7Maurizio Zibetti8Leonardo Lopiano9Department of Control and Computing Engineering, Politecnico di Torino, Torino, ItalyDepartment of Control and Computing Engineering, Politecnico di Torino, Torino, ItalyDepartment of Control and Computing Engineering, Politecnico di Torino, Torino, ItalyDepartment of Control and Computing Engineering, Politecnico di Torino, Torino, ItalyDepartment of Neuroscience “Rita Levi Montalcini,”, University of Turin, Torino, ItalyDepartment of Neuroscience “Rita Levi Montalcini,”, University of Turin, Torino, ItalyDepartment of Neuroscience “Rita Levi Montalcini,”, University of Turin, Torino, ItalyDepartment of Neuroscience “Rita Levi Montalcini,”, University of Turin, Torino, ItalyDepartment of Neuroscience “Rita Levi Montalcini,”, University of Turin, Torino, ItalyDepartment of Neuroscience “Rita Levi Montalcini,”, University of Turin, Torino, Italy<italic>Goal:</italic> 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. <italic>Methods:</italic> 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. <italic>Results:</italic> 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. <italic>Conclusions:</italic> We proposed an objective and reliable tool for the automatic quantification of the MDS-UPDRS Leg Agility task. 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.https://ieeexplore.ieee.org/document/9090332/Artificial neural networksbradykinesialeg agilityparkinson's diseasesmartphone |
| spellingShingle | Luigi Borzi Marilena Varrecchia Stefano Sibille Gabriella Olmo Carlo Alberto Artusi Margherita Fabbri Mario Giorgio Rizzone Alberto Romagnolo Maurizio Zibetti Leonardo Lopiano Smartphone-Based Estimation of Item 3.8 of the MDS-UPDRS-III for Assessing Leg Agility in People With Parkinson's Disease IEEE Open Journal of Engineering in Medicine and Biology Artificial neural networks bradykinesia leg agility parkinson's disease smartphone |
| title | Smartphone-Based Estimation of Item 3.8 of the MDS-UPDRS-III for Assessing Leg Agility in People With Parkinson's Disease |
| title_full | Smartphone-Based Estimation of Item 3.8 of the MDS-UPDRS-III for Assessing Leg Agility in People With Parkinson's Disease |
| title_fullStr | Smartphone-Based Estimation of Item 3.8 of the MDS-UPDRS-III for Assessing Leg Agility in People With Parkinson's Disease |
| title_full_unstemmed | Smartphone-Based Estimation of Item 3.8 of the MDS-UPDRS-III for Assessing Leg Agility in People With Parkinson's Disease |
| title_short | Smartphone-Based Estimation of Item 3.8 of the MDS-UPDRS-III for Assessing Leg Agility in People With Parkinson's Disease |
| title_sort | smartphone based estimation of item 3 8 of the mds updrs iii for assessing leg agility in people with parkinson x0027 s disease |
| topic | Artificial neural networks bradykinesia leg agility parkinson's disease smartphone |
| url | https://ieeexplore.ieee.org/document/9090332/ |
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