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 Oct:12263:637-647.
doi: 10.1007/978-3-030-59716-0_61. Epub 2020 Sep 29.

Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity

Mandy Lu  1 , Kathleen Poston  2 , Adolf Pfefferbaum  2   3 , Edith V Sullivan  2 , Li Fei-Fei  1 , Kilian M Pohl  2   3 , Juan Carlos Niebles  1 , Ehsan Adeli  1   2
Affiliations

Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity

Mandy Lu et al. Med Image Comput Comput Assist Interv. 2020 Oct.

Abstract

Parkinson's disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or costly and intrusive wearables evaluating motor impairments. For the first time, we propose a computer vision-based model that observes non-intrusive video recordings of individuals, extracts their 3D body skeletons, tracks them through time, and classifies the movements according to the MDS-UPDRS gait scores. Experimental results show that our proposed method performs significantly better than chance and competing methods with an F 1-score of 0.83 and a balanced accuracy of 81%. This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity. Our work demonstrates how computer-assisted technologies can be used to non-intrusively monitor patients and their motor impairments. The code is available at https://github.com/mlu355/PD-Motor-Severity-Estimation.

Keywords: Computer Vision; Gait Analysis; Movement Disorder Society Unified Parkinson’s Disease Rating Scale.

PubMed Disclaimer

Figures

Fig.1:
Fig.1:
Progressive PD impairments demonstrated by 3D gait (poses fade over time; left/right distinguished by color) with MDS-UPDRS gait score shown below each skeleton. Participants are taken from our clinical dataset. Classes 0 to 2 progressively decrease in mobility with reduced arm swing and range of pedal motion (i.e., reduced stride amplitude and footlift) while class 3 becomes imbalanced.
Fig.2:
Fig.2:
The proposed framework: we first track the participant throughout the video and remove other persons, e.g., clinicians. Then, we extract the identified participants’ 3D body mesh and subsequently the skeletons. Finally, our proposed OF-DDNet estimates the MDS-UPDRS gait score based on only the 3D pose sequence.
Fig. 3:
Fig. 3:
Confusion matrix of OF-DDNet.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Adeli E, Shi F, An L, Wee CY, Wu G, Wang T, Shen D: Joint feature-sample selection and robust diagnosis of parkinson’s disease from mri data. NeuroImage 141, 206–219 (2016) - PMC - PubMed
    1. Andriluka M, Pishchulin L, Gehler P, Schiele B: 2d human pose estimation: New benchmark and state of the art analysis. In: Proceedings of the IEEE Conference on computer Vision and Pattern Recognition pp. 3686–3693 (2014)
    1. Bewley A, Ge Z, Ott L, Ramos F, Upcroft B: Simple online and realtime tracking In: ICIP. pp. 3464–3468. IEEE (2016)
    1. Bharti K, Suppa A, Tommasin S, Zampogna A, Pietracupa S, Berardelli A, Pantano P: Neuroimaging advances in parkinson’s disease with freezing of gait: A systematic review. NeuroImage: Clinical p. 102059 (2019) - PMC - PubMed
    1. Bogo F, Kanazawa A, Lassner C, Gehler P, Romero J, Black MJ: Keep it smpl: Automatic estimation of 3d human pose and shape from a single image In: ECCV. pp. 561–578. Springer; (2016)

LinkOut - more resources