ArticleSensors (Basel, Switzerland)2022
Statistical Analysis and Kinematic Assessment of Upper Limb Reaching Task in Parkinson's Disease.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Machine Learning Aided Kinematic Profiling of Reaching Movements Separates Spinocerebellar Ataxia type 12 and Essential Tremor.Cerebellum (London, England) · 2026Article
- Smart Watch Sensors for Tremor Assessment in Parkinson's Disease-Algorithm Development and Measurement Properties Analysis.Sensors (Basel, Switzerland) · 2025Article
- A Full-Body IMU-Based Motion Dataset of Daily Tasks by Older and Younger Adults.Scientific data · 2025Article
- A Statistical Approach for Functional Reach-to-Grasp Segmentation Using a Single Inertial Measurement Unit.Sensors (Basel, Switzerland) · 2024Article
- The Use of Head-Mounted Display Systems for Upper Limb Kinematic Analysis in Post-Stroke Patients: A Perspective Review on Benefits, Challenges and Other Solutions.Bioengineering (Basel, Switzerland) · 2024Review
- A neuromechanical model characterizing the motor planning and posture control in the voluntary lean in Parkinson's disease.Journal of neuroengineering and rehabilitation · 2024Article
- The effect of gravity on hand spatio-temporal kinematic features during functional movements.PloS one · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The impact of neurodegenerative disorders is twofold; they affect both quality of life and healthcare expenditure. In the case of Parkinson's disease, several strategies have been attempted to support the pharmacological treatment with rehabilitation protocols aimed at restoring motor function. In this scenario, the study of upper limb control mechanisms is particularly relevant due to the complexity of the joints involved in the movement of the arm. For these reasons, it is difficult to define proper indicators of the rehabilitation outcome. In this work, we propose a methodology to analyze and extract an ensemble of kinematic parameters from signals acquired during a complex upper limb reaching task. The methodology is tested in both healthy subjects and Parkinson's disease patients (N = 12), and a statistical analysis is carried out to establish the value of the extracted kinematic features in distinguishing between the two groups under study. The parameters with the greatest number of significances across the submovements are duration, mean velocity, maximum velocity, maximum acceleration, and smoothness. Results allowed the identification of a subset of significant kinematic parameters that could serve as a proof-of-concept for a future definition of potential indicators of the rehabilitation outcome in Parkinson's disease.
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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.