A generalizable and open-source algorithm for real-life monitoring of tremor in Parkinson’s disease

Abstract Wearable sensors can objectively and continuously monitor daily-life tremor in Parkinson’s Disease (PD). We developed an open-source algorithm for real-life monitoring of PD tremor which achieves generalizable performance across different wrist-worn devices. We achieved this using a unique...

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Main Authors: Nienke A. Timmermans, Roberta Terranova, Diogo C. Soriano, Hayriye Cagnan, Yordan P. Raykov, Ioan Gabriel Bucur, Bastiaan R. Bloem, Rick C. Helmich, Luc J. W. Evers
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:npj Parkinson's Disease
Online Access:https://doi.org/10.1038/s41531-025-01056-2
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author Nienke A. Timmermans
Roberta Terranova
Diogo C. Soriano
Hayriye Cagnan
Yordan P. Raykov
Ioan Gabriel Bucur
Bastiaan R. Bloem
Rick C. Helmich
Luc J. W. Evers
author_facet Nienke A. Timmermans
Roberta Terranova
Diogo C. Soriano
Hayriye Cagnan
Yordan P. Raykov
Ioan Gabriel Bucur
Bastiaan R. Bloem
Rick C. Helmich
Luc J. W. Evers
author_sort Nienke A. Timmermans
collection DOAJ
description Abstract Wearable sensors can objectively and continuously monitor daily-life tremor in Parkinson’s Disease (PD). We developed an open-source algorithm for real-life monitoring of PD tremor which achieves generalizable performance across different wrist-worn devices. We achieved this using a unique combination of two independent, complementary datasets. The first was a small, but extensively video-labeled gyroscope dataset collected during unscripted activities at home (n = 24 PD; n = 24 controls). We used this to train and validate a logistic regression tremor detector based on cepstral coefficients. The second was a large, unsupervised dataset (n = 517 PD; n = 50 controls, data collected for 2 weeks with a different device), used to externally validate the algorithm. Results show that our algorithm can reliably quantify real-life PD tremor (sensitivity of 0.61 (0.20) and specificity of 0.97 (0.05)). Weekly aggregated tremor time and power showed excellent test-retest reliability and moderate correlation to MDS-UPDRS rest tremor scores. This opens possibilities to support clinical trials and individual tremor management with wearable technology.
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spelling doaj-art-8296111a593845a6b76d2cff3a9a04862025-08-20T03:04:36ZengNature Portfolionpj Parkinson's Disease2373-80572025-07-0111111010.1038/s41531-025-01056-2A generalizable and open-source algorithm for real-life monitoring of tremor in Parkinson’s diseaseNienke A. Timmermans0Roberta Terranova1Diogo C. Soriano2Hayriye Cagnan3Yordan P. Raykov4Ioan Gabriel Bucur5Bastiaan R. Bloem6Rick C. Helmich7Luc J. W. Evers8Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Radboud University Medical Center, Institute for Brain, Cognition and BehaviorDepartment of Neurology, Center of Expertise for Parkinson and Movement Disorders, Radboud University Medical Center, Institute for Brain, Cognition and BehaviorMRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordMRC Brain Network Dynamics Unit, Nuffield Department of Clinical Neurosciences, University of OxfordSchool of Mathematical Sciences, University of NottinghamInstitute for Computing and Information Sciences, Radboud UniversityDepartment of Neurology, Center of Expertise for Parkinson and Movement Disorders, Radboud University Medical Center, Institute for Brain, Cognition and BehaviorDepartment of Neurology, Center of Expertise for Parkinson and Movement Disorders, Radboud University Medical Center, Institute for Brain, Cognition and BehaviorDepartment of Neurology, Center of Expertise for Parkinson and Movement Disorders, Radboud University Medical Center, Institute for Brain, Cognition and BehaviorAbstract Wearable sensors can objectively and continuously monitor daily-life tremor in Parkinson’s Disease (PD). We developed an open-source algorithm for real-life monitoring of PD tremor which achieves generalizable performance across different wrist-worn devices. We achieved this using a unique combination of two independent, complementary datasets. The first was a small, but extensively video-labeled gyroscope dataset collected during unscripted activities at home (n = 24 PD; n = 24 controls). We used this to train and validate a logistic regression tremor detector based on cepstral coefficients. The second was a large, unsupervised dataset (n = 517 PD; n = 50 controls, data collected for 2 weeks with a different device), used to externally validate the algorithm. Results show that our algorithm can reliably quantify real-life PD tremor (sensitivity of 0.61 (0.20) and specificity of 0.97 (0.05)). Weekly aggregated tremor time and power showed excellent test-retest reliability and moderate correlation to MDS-UPDRS rest tremor scores. This opens possibilities to support clinical trials and individual tremor management with wearable technology.https://doi.org/10.1038/s41531-025-01056-2
spellingShingle Nienke A. Timmermans
Roberta Terranova
Diogo C. Soriano
Hayriye Cagnan
Yordan P. Raykov
Ioan Gabriel Bucur
Bastiaan R. Bloem
Rick C. Helmich
Luc J. W. Evers
A generalizable and open-source algorithm for real-life monitoring of tremor in Parkinson’s disease
npj Parkinson's Disease
title A generalizable and open-source algorithm for real-life monitoring of tremor in Parkinson’s disease
title_full A generalizable and open-source algorithm for real-life monitoring of tremor in Parkinson’s disease
title_fullStr A generalizable and open-source algorithm for real-life monitoring of tremor in Parkinson’s disease
title_full_unstemmed A generalizable and open-source algorithm for real-life monitoring of tremor in Parkinson’s disease
title_short A generalizable and open-source algorithm for real-life monitoring of tremor in Parkinson’s disease
title_sort generalizable and open source algorithm for real life monitoring of tremor in parkinson s disease
url https://doi.org/10.1038/s41531-025-01056-2
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