ReviewJournal of neurology2024
Intelligent devices for assessing essential tremor: a comprehensive review.
Review in Journal of neurology, 2024. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
7 citing papers in PubMed.
- Intelligent differentiation between Parkinson's disease and essential tremor using wearable sensors and machine learning: a temporal validation study.BMC medical informatics and decision making · 2026Article
- Ultrasound-Informed State Estimation of Wrist Tremor Dynamics via Koopman Operator for Personalized Sensory Peripheral Nerve Stimulation.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026Article
- Essential Tremor Severity Assessment Using Handwriting Analysis and Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- Modern Phenomenology of Tremor: How Novel Technology Can Assist in the Diagnosis of Tremor.Movement disorders clinical practice · 2025Article
- Tremor: Clinical Frameworks, Network Dysfunction and Therapeutics.Brain sciences · 2025Review
- A deep learning approach to remotely assessing essential tremor with handwritten images.Scientific reports · 2025Article
- Gait-based Parkinson's disease diagnosis and severity classification using force sensors and machine learning.Scientific reports · 2025Article
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.
Funding
Abstract
Essential tremor (ET) stands as the most prevalent movement disorder, characterized by rhythmic and involuntary shaking of body parts. Achieving an accurate and comprehensive assessment of tremor severity is crucial for effectively diagnosing and managing ET. Traditional methods rely on clinical observation and rating scales, which may introduce subjective biases and hinder continuous evaluation of disease progression. Recent research has explored new approaches to quantifying ET. A promising method involves the use of intelligent devices to facilitate objective and quantitative measurements. These devices include inertial measurement units, electromyography, video equipment, and electronic handwriting boards, and more. Their deployment enables real-time monitoring of human activity data, featuring portability and efficiency. This capability allows for more extensive research in this field and supports the shift from in-lab/clinic to in-home monitoring of ET symptoms. Therefore, this review provides an in-depth analysis of the application, current development, potential characteristics, and roles of intelligent devices in assessing ET.
Indexed as
Identifiers
38816480What 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.