ArticleBiomedical engineering online2015
Temporal fluctuations of tremor signals from inertial sensor: a preliminary study in differentiating Parkinson's disease from essential tremor.
Article in Biomedical engineering online, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 36 citations in OpenAlex.
- Wearable sensor-based gait analysis to discriminate early Parkinson's disease from essential tremor.Journal of neurology · 2023Trial
- Essential Tremor and Digital Biomarkers: A Scoping Review Using the TRACE Framework to Map Readiness for Clinical Trials and Routine Practice.Tremor and other hyperkinetic movements (New York, N.Y.) · 2026Article
- Phenotypical Differentiation of Tremor Using Time Series Feature Extraction and Machine Learning.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- Smart Watch Sensors for Tremor Assessment in Parkinson's Disease-Algorithm Development and Measurement Properties Analysis.Sensors (Basel, Switzerland) · 2025Article
- Old problems, new solutions: harnessing technology and innovation in Parkinson's disease-evidence and experiences from Thailand.Journal of neural transmission (Vienna, Austria : 1996) · 2024Review
- Upper limb intention tremor assessment: opportunities and challenges in wearable technology.Journal of neuroengineering and rehabilitation · 2024Review
- Integrating Big Data, Artificial Intelligence, and motion analysis for emerging precision medicine applications in Parkinson's Disease.Journal of big data · 2024Article
- Diagnosis and Treatment of Tremor in Parkinson's Disease Using Mechanical Devices.Life (Basel, Switzerland) · 2022Review
- Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care.Phenomics (Cham, Switzerland) · 2022Review
- Wearable sensors during drawing tasks to measure the severity of essential tremor.Scientific reports · 2022Article
- Identification and Classification of Parkinsonian and Essential Tremors for Diagnosis Using Machine Learning Algorithms.Frontiers in neuroscience · 2022Article
- Screening of Parkinson's Disease Using Geometric Features Extracted from Spiral Drawings.Brain sciences · 2021Article
- Recognizing Manual Activities Using Wearable Inertial Measurement Units: Clinical Application for Outcome Measurement.Sensors (Basel, Switzerland) · 2021Article
- A-WEAR Bracelet for Detection of Hand Tremor and Bradykinesia in Parkinson's Patients.Sensors (Basel, Switzerland) · 2021Article
- Could Wearable and Mobile Technology Improve the Management of Essential Tremor?Frontiers in neurology · 2018Article
- Quantitative Analysis of Parkinsonian Tremor in a Clinical Setting Using Inertial Measurement Units.Parkinson's disease · 2018Article
- Tensometric tremorography in high-precision medical diagnostic systems.Medical devices (Auckland, N.Z.) · 2018Article
- Automatic Classification of Tremor Severity in Parkinson's Disease Using a Wearable Device.Sensors (Basel, Switzerland) · 2017Article
- Detecting dopaminergic neuronal degeneration using diffusion tensor imaging in a rotenone-induced rat model of Parkinson's disease: fractional anisotropy and mean diffusivity values.Neural regeneration research · 2017Article
- Differential diagnosis between Parkinson's disease and essential tremor using the smartphone's accelerometer.PloS one · 2017Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundParkinson's disease (PD) and essential tremor (ET) are the two most common movement disorders but the rate of misdiagnosis rate in these disorders is high due to similar characteristics of tremor. The purpose of the study is to present: (a) a solution to identify PD and ET patients by using the novel measurement of tremor signal variations while performing the resting task, (b) the improvement of the differentiation of PD from ET patients can be obtained by using the ratio of the novel measurement while performing two specific tasks.
methods35 PD and 22 ET patients were asked to participate in the study. They were asked to wear a 6-axis inertial sensor on his/her index finger of the tremor dominant hand and perform three tasks including kinetic, postural and resting tasks. Each task required 10 s to complete. The angular rate signal measured during the performance of these tasks was band-pass filtered and transformed into a two-dimensional representation. The ratio of the ellipse area covering 95 % of this two-dimensional representation of different tasks was investigated and the two best tasks were selected for the purpose of differentiation.
resultsThe ellipse area of two-dimensional representation of the resting task of PD and ET subjects are statistically significantly different (p < 0.05). Furthermore, the fluctuation ratio, defined as a ratio of the ellipse area of two-dimensional representation of resting to kinetic tremor, of PD subjects were statistically significantly higher than ET subjects in all axes (p = 0.0014, 0.0011 and 0.0001 for x, y and z-axis, respectively). The validation shows that the proposed method provides 100 % sensitivity, specificity and accuracy of the discrimination in the 5 subjects in the validation group. While the method would have to be validated with a larger number of subjects, these preliminary results show the feasibility of the approach.
conclusionsThis study provides the novel measurement of tremor variation in time domain termed 'temporal fluctuation'. The temporal fluctuation of the resting task can be used to discriminate PD from ET subjects. The ratio of the temporal fluctuation of the resting task to the kinetic task improves the reliability of the discrimination. While the method is powerful, it is also simple so it could be applied on low resource platforms such as smart phones and watches which are commonly equipped with inertial sensors.
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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.