ArticleSensors (Basel, Switzerland)2022
Machine Learning Approach to Support the Detection of Parkinson's Disease in IMU-Based Gait Analysis.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Deep learning models detect neuromuscular disease and quantify functional impairment from video-derived biomechanics data.bioRxiv : the preprint server for biology · 2026Article
- MediaPipe-Based Activity Analysis by Healthcare Professionals: Method Development and Technology Acceptance-A Pilot Study.Sensors (Basel, Switzerland) · 2026Article
- Integrating statistical parametric mapping, functional principal component analysis, explainable machine learning, and equivalence testing for multimodal gait signal analysis.BMC biomedical engineering · 2026Article
- Step Count as a Digital Mobility Outcome-A Pilot Study on Differences Between the Upper and Lower Extremities.Sensors (Basel, Switzerland) · 2026Observational
- A Single-IMU Wearable System with 1D U-Net for Knee Adduction Moment Waveform Reconstruction During Gait.Sensors (Basel, Switzerland) · 2026Article
- Current Trends in AI Gait Analysis for the Detection and Assessment of Parkinson's Disease Severity: Systematic Review and Meta-Analysis of Performance Using Logit Transformation.Healthcare (Basel, Switzerland) · 2026Review
- Identifying key gait features in stroke patients using wearable inertial sensors and supervised and unsupervised machine learning.Scientific reports · 2026Article
- Identifying neuromuscular and mental fatigue in elite youth table tennis players using machine learning.Scientific reports · 2026Article
- Similarity Gait Networks with XAI for Parkinson's Disease Classification: A Pilot Study.Bioengineering (Basel, Switzerland) · 2026Article
- Identifying and predicting gait stability metrics in people with stroke in uneven-surface walking using machine learning.Scientific reports · 2026Article
- Analyzing Gait Pattern Associated With Neuropsychiatric Symptoms in Parkinson's Disease by a Comprehensive Approach.IEEE journal of translational engineering in health and medicine · 2026Article
- A deep learning pipeline for detecting vestibular schwannoma patients with unilateral vestibular loss based on kinematic data.Scientific reports · 2025Article
- Parkinson's disease severity clustering based on gait activity from mobile device.Scientific reports · 2025Article
- Automated UPDRS Gait Scoring Using Wearable Sensor Fusion and Deep Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Development of machine learning models for gait-based classification of incomplete spinal cord injuries and cauda equina syndrome.Scientific reports · 2025Article
- Retrospective Frailty Assessment in Older Adults Using Inertial Measurement Unit-Based Deep Learning on Gait Spectrograms.Sensors (Basel, Switzerland) · 2025Article
- Artificial Intelligence in the Diagnosis of Neurological Diseases Using Biomechanical and Gait Analysis Data: A Scopus-Based Bibliometric Analysis.Neurology international · 2025Review
- Test-Retest Reliability and Minimal Detectable Changes for Wearable Sensor-Derived Gait Stability, Symmetry, and Smoothness in Individuals with Severe Traumatic Brain Injury.Sensors (Basel, Switzerland) · 2025Article
- Machine learning for early detection and severity classification in people with Parkinson's disease.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this study was to determine which supervised machine learning (ML) algorithm can most accurately classify people with Parkinson's disease (pwPD) from speed-matched healthy subjects (HS) based on a selected minimum set of IMU-derived gait features. Twenty-two gait features were extrapolated from the trunk acceleration patterns of 81 pwPD and 80 HS, including spatiotemporal, pelvic kinematics, and acceleration-derived gait stability indexes. After a three-level feature selection procedure, seven gait features were considered for implementing five ML algorithms: support vector machine (SVM), artificial neural network, decision trees (DT), random forest (RF), and K-nearest neighbors. Accuracy, precision, recall, and F1 score were calculated. SVM, DT, and RF showed the best classification performances, with prediction accuracy higher than 80% on the test set. The conceptual model of approaching ML that we proposed could reduce the risk of overrepresenting multicollinear gait features in the model, reducing the risk of overfitting in the test performances while fostering the explainability of the results.
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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.