ArticleJournal of neuroengineering and rehabilitation2020
Classification of Parkinson's disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach.
Article in Journal of neuroengineering and rehabilitation, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 3 of them syntheses that pooled it.
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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
43 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Sarcopenia screening based on the assessment of gait with inertial measurement units: a systematic review.BMC geriatrics · 2024Pooled it
- Speech, voice, and language outcomes following deep brain stimulation: A systematic review.PloS one · 2024Pooled it
- Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Wearable sensor-based gait analysis to discriminate early Parkinson's disease from essential tremor.Journal of neurology · 2023Trial
- From phenotype to biology: a multi-modal roadmap of biofluid, tissue, imaging, and digital biomarkers in Parkinson's disease.Translational neurodegeneration · 2026Review
- Daily fall risk assessment for older adults using a balance sensor with machine learning.Frontiers in public health · 2026Article
- Toward future diagnostics of Parkinson's disease: a perspective on multimodal motor assessment and personalized digital twins.Frontiers in aging neuroscience · 2026Article
- Classification of fall risk in Parkinson's disease using empirical mode decomposition and machine learning.Frontiers in bioengineering and biotechnology · 2026Article
- Machine Learning for Differentiating Essential Tremor: A Scoping Review.Tremor and other hyperkinetic movements (New York, N.Y.) · 2026Article
- From fragmented neurotechnologies to closed-loop brain health systems: integrating artificial intelligence, digital health, digital twins, and advanced materials for brain disorders.Frontiers in bioengineering and biotechnology · 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
- Smartphone-derived multidomain features including voice, finger-tapping movement and gait aid early identification of Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Applying Wearable Sensors and Machine Learning to the Diagnostic Challenge of Distinguishing Parkinson's Disease from Other Forms of Parkinsonism.Biomedicines · 2025Article
- Efficient quantification of Parkinson's disease severity using augmented time-series data.PloS one · 2025Article
- Balance and fall-risk assessment in older adults using wearable plantar pressure and semi-supervised learning.Frontiers in bioengineering and biotechnology · 2025Article
- A machine learning approach for the design optimization of a multiple magnetic and inertial sensors wearable system for the spine mobility assessment.Journal of neuroengineering and rehabilitation · 2024Article
- Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease.Sensors (Basel, Switzerland) · 2024Article
- Intelligent devices for assessing essential tremor: a comprehensive review.Journal of neurology · 2024Review
- Multi-Shared-Task Self-Supervised CNN-LSTM for Monitoring Free-Body Movement UPDRS-III Using Wearable Sensors.Bioengineering (Basel, Switzerland) · 2024Article
- Machine Learning Models for Parkinson Disease: Systematic Review.JMIR medical informatics · 2024Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundParkinson's disease (PD) and essential tremor (ET) are movement disorders that can have similar clinical characteristics including tremor and gait difficulty. These disorders can be misdiagnosed leading to delay in appropriate treatment. The aim of the study was to determine whether balance and gait variables obtained with wearable inertial motion sensors can be utilized to differentiate between PD and ET using machine learning. Additionally, we compared classification performances of several machine learning models.
methodsThis retrospective study included balance and gait variables collected during the instrumented stand and walk test from people with PD (n = 524) and with ET (n = 43). Performance of several machine learning techniques including neural networks, support vector machine, k-nearest neighbor, decision tree, random forest, and gradient boosting, were compared with a dummy model or logistic regression using F1-scores.
resultsMachine learning models classified PD and ET based on balance and gait characteristics better than the dummy model (F1-score = 0.48) or logistic regression (F1-score = 0.53). The highest F1-score was 0.61 of neural network, followed by 0.59 of gradient boosting, 0.56 of random forest, 0.55 of support vector machine, 0.53 of decision tree, and 0.49 of k-nearest neighbor.
conclusionsThis study demonstrated the utility of machine learning models to classify different movement disorders based on balance and gait characteristics collected from wearable sensors. Future studies using a well-balanced data set are needed to confirm the potential clinical utility of machine learning models to discern between PD and ET.
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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.