Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 May 8:1:140-147.
doi: 10.1109/OJEMB.2020.2993463. eCollection 2020.

Smartphone-Based Estimation of Item 3.8 of the MDS-UPDRS-III for Assessing Leg Agility in People With Parkinson's Disease

Luigi Borzi  1 , Marilena Varrecchia  1 , Stefano Sibille  1 , Gabriella Olmo  1 , Carlo Alberto Artusi  2 , Margherita Fabbri  2 , Mario Giorgio Rizzone  2 , Alberto Romagnolo  2 , Maurizio Zibetti  2 , Leonardo Lopiano  2
Affiliations

Smartphone-Based Estimation of Item 3.8 of the MDS-UPDRS-III for Assessing Leg Agility in People With Parkinson's Disease

Luigi Borzi et al. IEEE Open J Eng Med Biol. .

Abstract

Goal: 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: 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.

Keywords: Artificial neural networks; bradykinesia; leg agility; parkinson's disease; smartphone.

PubMed Disclaimer

Figures

Fig. 1.
Fig. 1.
Smartphone position adopted for the LA task scoring.
Fig. 2.
Fig. 2.
Distribution of the MDS-UPDRS scores assigned to the LA tasks. 0: normal. 1: slight. 2: mild. 3: moderate. 4: severe.
Fig. 3.
Fig. 3.
Feature ranking based on Pearson's correlation coefficient (r). C1, C2, C3 identify gaps in r-values of adjacent features.
Fig. 4.
Fig. 4.
Histogram of distance between mean clinicians score and ANN outcomes. Continuous values were taken into account for assessment.
Fig. 5.
Fig. 5.
Bland-Altman Plot of mean clinicians score and ANN outcomes.
Fig. 6.
Fig. 6.
For each evaluating clinician, score distribution among UPDRS-part III Leg-Agility score (CLx stands for score x, x = LA score)
See this image and copyright information in PMC

References

    1. Jankovic J., “Parkinson's disease: Clinical features and diagnosis,” J. Neurology, Neurosurgery Psychiatry, vol. 79, no. 4, pp. 368–376, 2008. - PubMed
    1. Elbaz A., Carcaillon L., Kab S., and Moisanc F., “Epidemiology of Parkinson's disease,” Elsevier Revue Neurologique, vol. 172, no. 1, pp. 14–26, 2016. - PubMed
    1. Aquino C. C. and Fox S. H., “Clinical spectrum of levodopa induced complications,” Movement Disorders, vol. 30, no. 1, pp. 80–89, 2015. - PubMed
    1. Klucken J., Krüger R., Schmidt P., and Bloem B., “Management of Parkinson's disease 20 years from now: Towards digital health pathways,” J. Parkinson's Disease, vol. 8, pp. S85–S94, 2018. - PMC - PubMed
    1. Martínez-Martín P., Gil-Nagel A., Gracia L. M., Gómez J. B., Martínez-Sarriés J., and Bermejo F., “Unified Parkinson's disease rating scale characteristics and structure,” Movement Disorders, vol. 9, no. 1, pp. 76–83, 1994. - PubMed

LinkOut - more resources