# A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities

arXiv: https://arxiv.org/abs/2505.00525 · HTML: https://mlsanity.com/paper/2505.00525/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 340 references that are in the map.
- First posted on arXiv 1 May 2025.

## Builds on (most cited first, 24 of 24)

- [Deep Learning-Based Gait Recognition Using Smartphones in the Wild](https://mlsanity.com/paper/1811.00338/) (2018)
- [PECoP: Parameter Efficient Continual Pretraining for Action Quality Assessment](https://mlsanity.com/paper/2311.07603/) (2023)
- [Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait](https://mlsanity.com/paper/1910.11509/) (2019)
- [EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network](https://mlsanity.com/paper/2011.12107/) (2020)
- [Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition](https://mlsanity.com/paper/2204.01670/) (2022)
- [Computer keyboard interaction as an indicator of early Parkinson's disease](https://mlsanity.com/paper/1604.08620/) (2016)
- [Multimodal Gait Recognition for Neurodegenerative Diseases](https://mlsanity.com/paper/2101.02469/) (2021)
- [X-vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection from Speech](https://mlsanity.com/paper/2007.03599/) (2020)
- [Associated Spatio-Temporal Capsule Network for Gait Recognition](https://mlsanity.com/paper/2101.02458/) (2021)
- [Predicting Parkinson's Disease using Latent Information extracted from Deep Neural Networks](https://mlsanity.com/paper/1901.07822/) (2019)
- [Exploring Motion Boundaries in an End-to-End Network for Vision-based Parkinson's Severity Assessment](https://mlsanity.com/paper/2012.09890/) (2020)
- [Transformers for 1D Signals in Parkinson's Disease Detection from Gait](https://mlsanity.com/paper/2204.00423/) (2022)
- [Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease](https://mlsanity.com/paper/2411.03044/) (2024)
- [HCT: Hybrid Convnet-Transformer for Parkinson's disease detection and severity prediction from gait](https://mlsanity.com/paper/2310.17078/) (2023)
- [Machine Learning-Based Differential Diagnosis of Parkinson's Disease Using Kinematic Feature Extraction and Selection](https://mlsanity.com/paper/2501.02014/) (2025)
- [Detecting Neurodegenerative Diseases using Frame-Level Handwriting Embeddings](https://mlsanity.com/paper/2502.07025/) (2025)
- [An Improved Approach for Prediction of Parkinson's Disease using Machine Learning Techniques](https://mlsanity.com/paper/1610.08250/) (2016)
- [Remote Pathological Gait Classification System](https://mlsanity.com/paper/2105.01634/) (2021)
- [Gait Disorder Assessment Based on a Large-Scale Clinical Trial: WiFi vs. Video vs. Doctor's Visual Inspection](https://mlsanity.com/paper/2502.05328/) (2025)
- [Evidence for Phenotype-Driven Disparities in Freezing of Gait Detection and Approaches to Bias Mitigation](https://mlsanity.com/paper/2502.09626/) (2025)
- [Artificial intelligence-enabled detection and assessment of Parkinson's disease using multimodal data: A survey](https://mlsanity.com/paper/2502.10703/) (2025)
- [Parkinsonian Chinese Speech Analysis towards Automatic Classification of Parkinson's Disease](https://mlsanity.com/paper/2105.14704/) (2021)
- [Graph Neural Networks for Parkinsons Disease Detection](https://mlsanity.com/paper/2409.07884/) (2024)
- [Oscillatory Signatures of Parkinson's Disease: Central and Parietal EEG Alterations Across Multiple Frequency Bands](https://mlsanity.com/paper/2503.12392/) (2025)

## Built on by (most cited first, 4 of 4)

- [ARISE: Agentic Rubric-Guided Iterative Survey Engine for Automated Scholarly Paper Generation](https://mlsanity.com/paper/2511.17689/) (2025)
- [Brain Tumor Classification from MRI Scans via Transfer Learning and Enhanced Feature Representation](https://mlsanity.com/paper/2509.22956/) (2025)
- [Multi Class Parkinson Disease Detection Based on Finger Tapping Using Attention Enhanced CNN BiLSTM](https://mlsanity.com/paper/2510.10121/) (2025)
- [Effect of Activation Function and Model Optimizer on the Performance of Human Activity Recognition System Using Various Deep Learning Models](https://mlsanity.com/paper/2512.20104/) (2025)

## Abstract (arXiv)

Parkinsons Disease (PD) is a progressive neurological disorder that primarily affects motor functions and can lead to mild cognitive impairment (MCI) and dementia in its advanced stages. With approximately 10 million people diagnosed globally 1 to 1.8 per 1,000 individuals, according to reports by the Japan Times and the Parkinson Foundation early and accurate diagnosis of PD is crucial for improving patient outcomes. While numerous studies have utilized machine learning (ML) and deep learning (DL) techniques for PD recognition, existing surveys are limited in scope, often focusing on single data modalities and failing to capture the potential of multimodal approaches. To address these gaps, this study presents a comprehensive review of PD recognition systems across diverse data modalities, including Magnetic Resonance Imaging (MRI), gait-based pose analysis, gait sensory data, handwriting analysis, speech test data, Electroencephalography (EEG), and multimodal fusion techniques. Based on over 347 articles from leading scientific databases, this review examines key aspects such as data collection methods, settings, feature representations, and system performance, with a focus on recognition accuracy and robustness. This survey aims to serve as a comprehensive resource for researchers, providing actionable guidance for the development of next generation PD recognition systems. By leveraging diverse data modalities and cutting-edge machine learning paradigms, this work contributes to advancing the state of PD diagnostics and improving patient care through innovative, multimodal approaches.