# Using AI to Measure Parkinson's Disease Severity at Home

arXiv: https://arxiv.org/abs/2303.17573 · HTML: https://mlsanity.com/paper/2303.17573/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 30 Mar 2023.

## Builds on (most cited first, 1 of 1)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)

## Built on by (most cited first, 12 of 12)

- [Pose as Clinical Prior: Learning Dual Representations for Scoliosis Screening](https://mlsanity.com/paper/2509.00872/) (2025)
- [PARK: Parkinson's Analysis with Remote Kinetic-tasks](https://mlsanity.com/paper/2311.12654/) (2023)
- [PULSAR: Graph based Positive Unlabeled Learning with Multi Stream Adaptive Convolutions for Parkinson's Disease Recognition](https://mlsanity.com/paper/2312.05780/) (2023)
- [A Novel Fusion Architecture for PD Detection Using Semi-Supervised Speech Embeddings](https://mlsanity.com/paper/2405.17206/) (2024)
- [Hi5: Synthetic Data for Inclusive, Robust, Hand Pose Estimation](https://mlsanity.com/paper/2406.03599/) (2024)
- [Accessible, At-Home Detection of Parkinson's Disease via Multi-task Video Analysis](https://mlsanity.com/paper/2406.14856/) (2024)
- [Towards Relaxed Multimodal Inputs for Gait-based Parkinson's Disease Assessment](https://mlsanity.com/paper/2510.15748/) (2025)
- [Interpretable and Granular Video-Based Quantification of Motor Characteristics from the Finger Tapping Test in Parkinson Disease](https://mlsanity.com/paper/2506.18925/) (2025)
- [HiLWS: A Human-in-the-Loop Weak Supervision Framework for Curating Clinical and Home Video Data for Neurological Assessment](https://mlsanity.com/paper/2509.10557/) (2025)
- [Multi Class Parkinson Disease Detection Based on Finger Tapping Using Attention Enhanced CNN BiLSTM](https://mlsanity.com/paper/2510.10121/) (2025)
- [Wearable-informed generative digital avatars predict task-conditioned post-stroke locomotion](https://mlsanity.com/paper/2512.14329/) (2025)
- [Benchmarking Video Foundation Models for Remote Parkinson's Disease Screening](https://mlsanity.com/paper/2602.13507/) (2026)

## Abstract (arXiv)

We present an artificial intelligence system to remotely assess the motor performance of individuals with Parkinson's disease (PD). Participants performed a motor task (i.e., tapping fingers) in front of a webcam, and data from 250 global participants were rated by three expert neurologists following the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The neurologists' ratings were highly reliable, with an intra-class correlation coefficient (ICC) of 0.88. We developed computer algorithms to obtain objective measurements that align with the MDS-UPDRS guideline and are strongly correlated with the neurologists' ratings. Our machine learning model trained on these measures outperformed an MDS-UPDRS certified rater, with a mean absolute error (MAE) of 0.59 compared to the rater's MAE of 0.79. However, the model performed slightly worse than the expert neurologists (0.53 MAE). The methodology can be replicated for similar motor tasks, providing the possibility of evaluating individuals with PD and other movement disorders remotely, objectively, and in areas with limited access to neurological care.