ArticleScientific reports2020
High-Resolution Motor State Detection in Parkinson's Disease Using Convolutional Neural Networks.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 4 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- AI Video Analysis in Parkinson's Disease: A Systematic Review of the Most Accurate Computer Vision Tools for Diagnosis, Symptom Monitoring, and Therapy Management.Sensors (Basel, Switzerland) · 2025Pooled it
- Movement Disorders and Smart Wrist Devices: A Comprehensive Study.Sensors (Basel, Switzerland) · 2025Pooled it
- Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Wearable Technology to Detect Motor Fluctuations in Parkinson's Disease Patients: Current State and Challenges.Sensors (Basel, Switzerland) · 2021Pooled it
- Context-agnostic machine learning for Parkinson's disease motor symptom detection using wearable sensors.BMC medical informatics and decision making · 2026Article
- Adaptive Deep Brain Stimulation for Parkinson's Disease: Navigating the Roadblocks to Clinical Implementation.Movement disorders : official journal of the Movement Disorder Society · 2026Review
- The future digital outpatient clinic for movement disorders: from technological innovation to ethical and systemic implementation-a position paper.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Real-world experience with continuous subcutaneous foslevodopa/foscarbidopa infusion: insights and recommendations.Journal of neural transmission (Vienna, Austria : 1996) · 2026Article
- AI-Driven Analysis of Wrist-Worn Sensor Data for Monitoring Individual Treatment Response and Optimizing Levodopa Dosing in Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
- Opportunities and Limitations of Wrist-Worn Devices for Dyskinesia Detection in Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
- Estimating motor symptom presence and severity in Parkinson's disease from wrist accelerometer time series using ROCKET and InceptionTime.Scientific reports · 2025Article
- Systematic review of wearables assessing medication effect on motor function and symptoms in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Dual stream transformer for medication state classification in Parkinson's disease patients using facial videos.NPJ digital medicine · 2025Article
- Biochemical Sensors for Personalized Therapy in Parkinson's Disease: Where We Stand.Journal of clinical medicine · 2024Review
- [Commercial devices for monitoring symptoms in Parkinson's disease: benefits, limitations and trends].Revista de neurologia · 2024Review
- Multi-Shared-Task Self-Supervised CNN-LSTM for Monitoring Free-Body Movement UPDRS-III Using Wearable Sensors.Bioengineering (Basel, Switzerland) · 2024Article
- Utilizing deep learning models in an intelligent spiral drawing classification system for Parkinson's disease classification.Frontiers in medicine · 2024Article
- Application of Deep Learning Algorithm to Monitor Upper Extremity Task Practice.Sensors (Basel, Switzerland) · 2023Article
- Levodopa-Induced Dyskinesias in Parkinson's Disease: An Overview on Pathophysiology, Clinical Manifestations, Therapy Management Strategies and Future Directions.Journal of clinical medicine · 2023Review
- Feasibility and patient acceptability of a commercially available wearable and a smart phone application in identification of motor states in parkinson's disease.PLOS digital health · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Patients with advanced Parkinson's disease regularly experience unstable motor states. Objective and reliable monitoring of these fluctuations is an unmet need. We used deep learning to classify motion data from a single wrist-worn IMU sensor recording in unscripted environments. For validation purposes, patients were accompanied by a movement disorder expert, and their motor state was passively evaluated every minute. We acquired a dataset of 8,661 minutes of IMU data from 30 patients, with annotations about the motor state (OFF,ON, DYSKINETIC) based on MDS-UPDRS global bradykinesia item and the AIMS upper limb dyskinesia item. Using a 1-minute window size as an input for a convolutional neural network trained on data from a subset of patients, we achieved a three-class balanced accuracy of 0.654 on data from previously unseen subjects. This corresponds to detecting the OFF, ON, or DYSKINETIC motor state at a sensitivity/specificity of 0.64/0.89, 0.67/0.67 and 0.64/0.89, respectively. On average, the model outputs were highly correlated with the annotation on a per subject scale (r = 0.83/0.84; p < 0.0001), and sustained so for the highly resolved time windows of 1 minute (r = 0.64/0.70; p < 0.0001). Thus, we demonstrate the feasibility of long-term motor-state detection in a free-living setting with deep learning using motion data from a single IMU.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.