ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2023
Learning Hand Kinematics for Parkinson's Disease Assessment Using a Multimodal Sensor Glove.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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.
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Who cites it
16 citing papers in PubMed.
- Advances in Wearable Bioimaging.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Computational Insights Into Smart Bioelectronics in Digital Health Care (2020-2024): Topic Modeling Study.JMIR medical informatics · 2026Review
- Auditory cueing modulates motor performance and brain activity in early Parkinson's disease-a kinematic and fMRI study.NPJ Parkinson's disease · 2026Article
- AI-Enabled Flexible Sensing Ecosystems for Parkinson's Disease: Advancing Digital Biomarkers and Closed-Loop Interventions.Sensors (Basel, Switzerland) · 2026Review
- AI-Enabled Wearables for Motor Function Assessment and Rehabilitation in Parkinson Disease: Scoping Review.Journal of medical Internet research · 2026Review
- Machine learning-assisted point-of-care diagnostics for cardiovascular healthcare.Bioengineering & translational medicine · 2025Review
- Article
- Advances in 2D materials for wearable biomonitoring.Materials science & engineering. R, Reports : a review journal · 2025Article
- Advances in Machine Learning for Wearable Sensors.ACS nano · 2024Review
- Motion artefact management for soft bioelectronics.Nature reviews bioengineering · 2024Article
- Artificial Intelligence-Guided Gut-Microenvironment-Triggered Imaging Sensor Reveals Potential Indicators of Parkinson's Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review.Journal of personalized medicine · 2024Review
- Upper limb intention tremor assessment: opportunities and challenges in wearable technology.Journal of neuroengineering and rehabilitation · 2024Review
- Design and analysis of exoskeleton devices for rehabilitation of distal radius fracture.Frontiers in neurorobotics · 2024Article
- Learning Hand Kinematics for Parkinson's Disease Assessment Using a Multimodal Sensor Glove.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Wearable rehabilitation wristband for distal radius fractures.Frontiers in neuroscience · 2023Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Hand dysfunctions in Parkinson's disease include rigidity, muscle weakness, and tremor, which can severely affect the patient's daily life. Herein, a multimodal sensor glove is developed for quantifying the severity of Parkinson's disease symptoms in patients' hands while assessing the hands' multifunctionality. Toward signal processing, various algorithms are used to quantify and analyze each signal: Exponentially Weighted Average algorithm and Kalman filter are used to filter out noise, normalization to process bending signals, K-Means Cluster Analysis to classify muscle strength grades, and Back Propagation Neural Network to identify and classify tremor signals with an accuracy of 95.83%. Given the compelling features, the flexibility, muscle strength, and stability assessed by the glove and the clinical observations are proved to be highly consistent with Kappa values of 0.833, 0.867, and 0.937, respectively. The intraclass correlation coefficients obtained by reliability evaluation experiments for the three assessments are greater than 0.9, indicating that the system is reliable. The glove can be applied to assist in formulating targeted rehabilitation treatments and improve hand recovery efficiency.
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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.