Automated Shoulder Girdle Rigidity Assessment in Parkinson's Disease via an Integrated Model- and Data-Driven Approach.

Fatemeh Khosrobeygi, Zahra Abouhadi, Ailar Mahdizadeh, Ahmad Ashoori, Negin Niksirat, Maryam S Mirian, Martin J McKeown

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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2 · The registry

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5 · Who and what money

Authors and funding

7 authors.

Fatemeh KhosrobeygiSchool of Kinesiology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.

Zahra AbouhadiControl and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran 14155-6619, Iran.

Ailar MahdizadehDepartment of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.

Ahmad AshooriPacific Parkinson's Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada.

Negin NiksiratFaculty of Medicine, McGill University, Montreal, QC H3A 0G4, Canada.

Maryam S MirianPacific Parkinson's Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada.ORCID 0000-0001-5411-9902

Martin J McKeownPacific Parkinson's Research Centre, Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC V6T 2B5, Canada.ORCID 0000-0002-4048-0817

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parkinson's disease (PD) is characterized by motor symptoms, with key diagnostic features, such as rigidity, traditionally assessed through subjective clinical scales. This study proposes a novel hybrid framework integrating model-driven biomechanical features (damping ratio, decay rate) and data-driven statistical features (maximum detail coefficient) from wearable sensor data during a modified pendulum test to quantify shoulder girdle rigidity objectively. Using weak supervision, these features were unified to generate robust labels from limited data, achieving a 10% improvement in PD/healthy control classification accuracy (0.71 vs. 0.64) over data-driven methods and matching model-driven performance (0.70). The damping ratio and decay rate, aligning with Wartenberg pendulum test metrics like relaxation index, revealed velocity-dependent aspects of rigidity, challenging its clinical characterization as velocity-independent. Outputs correlated strongly with UPDRS rigidity scores (

Indexed as

Muscle RigidityParkinson DiseaseShoulderAgedBiomechanical PhenomenaFemaleHumansMaleMiddle AgedWearable Electronic Devicesdamping ratiodecay rateLTI modelnatural frequencyParkinson’s diseaserigidity assessmentweak supervision

Identifiers

PMID41094842

DOI10.3390/s25196019

PMCPMC12526519

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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.