ArticleScientific reports2022
Wearable sensors during drawing tasks to measure the severity of essential tremor.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 33 citations in OpenAlex.
- Essential Tremor Severity Assessment Using Handwriting Analysis and Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- A deep learning approach to remotely assessing essential tremor with handwritten images.Scientific reports · 2025Article
- Intelligent devices for assessing essential tremor: a comprehensive review.Journal of neurology · 2024Review
- A machine-learning method isolating changes in wrist kinematics that identify age-related changes in arm movement.Scientific reports · 2024Article
- Upper limb intention tremor assessment: opportunities and challenges in wearable technology.Journal of neuroengineering and rehabilitation · 2024Review
- Wearable Accelerometer and Gyroscope Sensors for Estimating the Severity of Essential Tremor.IEEE journal of translational engineering in health and medicine · 2024Article
- Development of a New Wearable Device for the Characterization of Hand Tremor.Bioengineering (Basel, Switzerland) · 2023Article
- Diagnosis Versus Classification of Essential Tremor: A Research Perspective.Journal of movement disorders · 2023Article
- Improving puncture accuracy in percutaneous CT-guided needle insertion with wireless inertial measurement unit: a phantom study.European radiology · 2023Article
- A Review of Commercial and Non-Commercial Wearables Devices for Monitoring Motor Impairments Caused by Neurodegenerative Diseases.Biosensors · 2022Review
- Assessment of movement disorders using wearable sensors during upper limb tasks: A scoping review.Frontiers in robotics and AI · 2022Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Commonly used methods to assess the severity of essential tremor (ET) are based on clinical observation and lack objectivity. This study proposes the use of wearable accelerometer sensors for the quantitative assessment of ET. Acceleration data was recorded by inertial measurement unit (IMU) sensors during sketching of Archimedes spirals in 17 ET participants and 18 healthy controls. IMUs were placed at three points (dorsum of hand, posterior forearm, posterior upper arm) of each participant's dominant arm. Movement disorder neurologists who were blinded to clinical information scored ET patients on the Fahn-Tolosa-Marin rating scale (FTM) and conducted phenotyping according to the recent Consensus Statement on the Classification of Tremors. The ratio of power spectral density of acceleration data in 4-12 Hz to 0.5-4 Hz bands and the total duration of the action were inputs to a support vector machine that was trained to classify the ET subtype. Regression analysis was performed to determine the relationship of acceleration and temporal data with the FTM scores. The results show that the sensor located on the forearm had the best classification and regression results, with accuracy of 85.71% for binary classification of ET versus control. There was a moderate to good correlation (r
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