ArticleSensors (Basel, Switzerland)2024
Sensor-Based Quantification of MDS-UPDRS III Subitems in Parkinson's Disease Using Machine Learning.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 10 citations in OpenAlex.
- Quantifying Slowness in Parkinson Disease Using a Serious Game: Cross-Sectional Study.JMIR serious games · 2026Article
- Clinical Validity of Wrist- and Trunk-Worn Sensor-Derived Data Models in Parkinson's Disease.Parkinson's disease · 2026Article
- Association between wrist-worn actigraphy and the MDS-UPDRS Parkinson's disease rating scale through machine learning: an exploratory study.Frontiers in digital health · 2026Article
- Lower limb motor effects of DBS neurofeedback in Parkinson's disease assessed through IMU-based UPDRS movement quality metrics.Scientific reports · 2025Article
- EEG Sensor-Based Parkinson's Disease Detection Using a Multi-Domain Feature Fusion Network.Sensors (Basel, Switzerland) · 2025Article
- Precision of an Inertial System to Evaluate the Finger Tapping Test in Women with Fibromyalgia.Sports (Basel, Switzerland) · 2025Article
- Automated UPDRS Gait Scoring Using Wearable Sensor Fusion and Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Advancing Parkinson's disease detection through multi-dimensional machine learning: a comprehensive framework using wearable movement sensor analytics.Frontiers in physiology · 2025Article
- Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Wearable sensors could be beneficial for the continuous quantification of upper limb motor symptoms in people with Parkinson's disease (PD). This work evaluates the use of two inertial measurement units combined with supervised machine learning models to classify and predict a subset of MDS-UPDRS III subitems in PD. We attached the two compact wearable sensors on the dorsal part of each hand of 33 people with PD and 12 controls. Each participant performed six clinical movement tasks in parallel with an assessment of the MDS-UPDRS III. Random forest (RF) models were trained on the sensor data and motor scores. An overall accuracy of 94% was achieved in classifying the movement tasks. When employed for classifying the motor scores, the averaged area under the receiver operating characteristic values ranged from 68% to 92%. Motor scores were additionally predicted using an RF regression model. In a comparative analysis, trained support vector machine models outperformed the RF models for specific tasks. Furthermore, our results surpass the literature in certain cases. The methods developed in this work serve as a base for future studies, where home-based assessments of pharmacological effects on motor function could complement regular clinical assessments.
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What OpenQuestion holds
Registered trials
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.