ArticleFrontiers in human neuroscience2022
Machine Learning Classifiers to Evaluate Data From Gait Analysis With Depth Cameras in Patients With Parkinson's Disease.
Article in Frontiers in human neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Markerless Motion Capture to Quantify Functional Performance in Neurodegeneration: Systematic Review.JMIR aging · 2024Pooled it
- Vision-Based Assessment of Skeletal Muscle Decline: Correlating Gait Variance with SPPB Performance.Healthcare (Basel, Switzerland) · 2025Article
- Smartphone-derived multidomain features including voice, finger-tapping movement and gait aid early identification of Parkinson's disease.NPJ Parkinson's disease · 2025Article
- A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis.Sensors (Basel, Switzerland) · 2025Review
- Evaluation of Arm Swing Features and Asymmetry during Gait in Parkinson's Disease Using the Azure Kinect Sensor.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
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Abstract
Introduction: The assessments of the motor symptoms in Parkinson's disease (PD) are usually limited to clinical rating scales (MDS UPDRS III), and it depends on the clinician's experience. This study aims to propose a machine learning technique algorithm using the variables from upper and lower limbs, to classify people with PD from healthy people, using data from a portable low-cost device (RGB-D camera). And can be used to support the diagnosis and follow-up of patients in developing countries and remote areas. Methods: We used Kinect Results: The Random Forest model is the most accurate for all three variable Datasets (Dataset A: 81.8%; Dataset B: 83.6%; Dataset C: 84.5%) followed by the support vector machine. The CIM shows a relation between leg variables and the arms swing asymmetry (ASA) and a proportional relationship between ASA and the diagnosis of PD with a robust estimator (1,537). Conclusions: Machine learning techniques based on objective measures using portable low-cost devices (Kinect
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