Trial reportJournal of neurology2023
Wearable sensor-based gait analysis to discriminate early Parkinson's disease from essential tremor.
Trial report in Journal of neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 3 of them syntheses that pooled it.
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
36 citing papers in PubMed, 3 syntheses or guidelines pooled it, 61 citations in OpenAlex.
- Evaluating the Utility of Wearable Sensors for the Early Diagnosis of Parkinson Disease: Systematic Review.Journal of medical Internet research · 2025Pooled it
- The experience and perception of wearable devices in Parkinson's disease patients: a systematic review and meta-synthesis of qualitative studies.Journal of neurology · 2025Pooled it
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Deep Learning-Based Parkinson's Disease Classification Using RGB Plantar Pressure Gait Images: A Comparative Study of CNN and Transformer Architectures.Diagnostics (Basel, Switzerland) · 2026Article
- Gait Analysis for Identifying Normal Cognition, Subjective Cognitive Decline, and Mild Cognitive Impairment in Parkinson Disease: Diagnostic Study.JMIR mHealth and uHealth · 2026Article
- From phenotype to biology: a multi-modal roadmap of biofluid, tissue, imaging, and digital biomarkers in Parkinson's disease.Translational neurodegeneration · 2026Review
- Wearable-sensor based walking and non-walking measures as progression markers in early to mid-stage Parkinson's disease.NPJ Parkinson's disease · 2026Article
- Machine learning classification of early-stage Parkinson's disease using sit-to-walk biomechanical features.Scientific reports · 2026Article
- Reconsidering Placebo Effects in Neuromodulation for Parkinson's Disease: Lessons for Clinical Trials and Therapeutic Translation.Biomedicines · 2026Review
- Discriminating vascular parkinsonism from early-stage postural instability gait difficulty subtype dominant Parkinson's disease: a dual-task gait analysis using wearable sensors.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- Machine Learning for Differentiating Essential Tremor: A Scoping Review.Tremor and other hyperkinetic movements (New York, N.Y.) · 2026Article
- 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
- IMU-based gait analysis methods: a systematic review of techniques for different body locations.Frontiers in sports and active living · 2026Review
- Wearable devices in neurological disorders: a narrative review of status quo and perspectives.Annals of translational medicine · 2025Review
- High-Accuracy Classification of Parkinson's Disease Using Ensemble Machine Learning and Stabilometric Biomarkers.Neurology international · 2025Article
- Association Between Turn Impairments and Cognitive Function in Parkinson Disease.Aging medicine (Milton (N.S.W)) · 2025Article
- Artificial Intelligence in the Diagnosis of Neurological Diseases Using Biomechanical and Gait Analysis Data: A Scopus-Based Bibliometric Analysis.Neurology international · 2025Review
- Applying Wearable Sensors and Machine Learning to the Diagnostic Challenge of Distinguishing Parkinson's Disease from Other Forms of Parkinsonism.Biomedicines · 2025Article
- Nonmotor Symptoms Differ Between Essential Tremor and Tremor-Dominant Parkinson's Disease.Brain and behavior · 2025Article
- Do the gait domains change in PD patients with freezing of gait during their 'interictal' period?BMC geriatrics · 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
8 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundDifferentiating early-stage Parkinson's disease (PD) from essential tremor (ET) is challenging since they have some overlapping clinical features. Since early-stage PD may present with slight gait impairment and ET generally does not, gait analysis could be used to differentiate PD from ET using machine learning.
objectiveTo differentiate early-stage PD from ET via machine learning using gait and postural transition parameters calculated using the raw kinematic signal captured from inertial measurement unit (IMU) sensors.
methodsGait and postural transition parameters were collected from 84 early-stage PD and 80 ET subjects during the Time Up and Go (TUG) test. We randomly split our data into training and test data. Within the training data, we separated the TUG test into four components: standing, straight walk, turning, and sitting to build weighted average ensemble classification models. The four components' weight indices were trained using logistic regression. Several ensemble models' leave-one-out cross-validation (LOOCV) performances were compared. Independent test data were used to evaluate the model with the best LOOCV performance.
resultsThe best weighted average ensemble classification model LOOCV results included an accuracy of 84%, Kappa of 0.68, sensitivity of 85.9%, specificity of 82.1%, and AUC of 0.912. Thirty-three gait and postural transition parameters, such as Arm-Symbolic Symmetry Index and 180° Turn-Max Angular Velocity, were included in Feature Group III. The independent test data achieved a 75.8% accuracy.
conclusionsOur findings suggest that gait and postural transition parameters obtained from wearable sensors combined with machine learning had the potential to distinguish between early-stage PD and ET.
Indexed as
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What 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.