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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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Open Journal of Engineering in Medicine and Biology |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9090332/ |
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| Summary: | <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. |
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| ISSN: | 2644-1276 |