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

arXiv: https://arxiv.org/abs/2007.08920 · HTML: https://mlsanity.com/paper/2007.08920/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 18 of 18)

- [Home Action Genome: Cooperative Compositional Action Understanding](https://mlsanity.com/paper/2105.05226/) (2021)
- [GaitForeMer: Self-Supervised Pre-Training of Transformers via Human Motion Forecasting for Few-Shot Gait Impairment Severity Estimation](https://mlsanity.com/paper/2207.00106/) (2022)
- [Estimating Parkinsonism Severity in Natural Gait Videos of Older Adults with Dementia](https://mlsanity.com/paper/2105.03464/) (2021)
- [GAITGen: Disentangled Motion-Pathology Impaired Gait Generative Model -- Bringing Motion Generation to the Clinical Domain](https://mlsanity.com/paper/2503.22397/) (2025)
- [Benchmarking Skeleton-based Motion Encoder Models for Clinical Applications: Estimating Parkinson's Disease Severity in Walking Sequences](https://mlsanity.com/paper/2405.17817/) (2024)
- [An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment](https://mlsanity.com/paper/2307.13108/) (2023)
- [Enhancing Gait Video Analysis in Neurodegenerative Diseases by Knowledge Augmentation in Vision Language Model](https://mlsanity.com/paper/2403.13756/) (2024)
- [Your Turn: At Home Turning Angle Estimation for Parkinson's Disease Severity Assessment](https://mlsanity.com/paper/2408.08182/) (2024)
- [AGIR: Assessing 3D Gait Impairment with Reasoning based on LLMs](https://mlsanity.com/paper/2503.18141/) (2025)
- [Pose-based Tremor Classification for Parkinson's Disease Diagnosis from Video](https://mlsanity.com/paper/2207.06828/) (2022)
- [Artificial intelligence-enabled detection and assessment of Parkinson's disease using multimodal data: A survey](https://mlsanity.com/paper/2502.10703/) (2025)
- [Pose as Clinical Prior: Learning Dual Representations for Scoliosis Screening](https://mlsanity.com/paper/2509.00872/) (2025)
- [Deep Learning Pose Estimation for Multi-Label Recognition of Combined Hyperkinetic Movement Disorders](https://mlsanity.com/paper/2602.00163/) (2026)
- [ErgoExplorer: Interactive Ergonomic Risk Assessment from Video Collections](https://mlsanity.com/paper/2209.05252/) (2022)
- [InceptoFormer: A Multi-Signal Neural Framework for Parkinson's Disease Severity Evaluation from Gait](https://mlsanity.com/paper/2508.04540/) (2025)
- [KRAST: Knowledge-Augmented Robotic Action Recognition with Structured Text for Vision-Language Models](https://mlsanity.com/paper/2509.16452/) (2025)
- [RePose: A Real-Time 3D Human Pose Estimation and Biomechanical Analysis Framework for Rehabilitation](https://mlsanity.com/paper/2601.00625/) (2026)
- [EgoInertia-MI: A Multimodal Egocentric Vision and IMU Benchmark for Motor Impairment Assessment](https://mlsanity.com/paper/2607.03934/) (2026)

## Abstract (arXiv)

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 F1-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.