ArticleDiagnostics (Basel, Switzerland)2021
Local Pattern Transformation Based Feature Extraction for Recognition of Parkinson's Disease Based on Gait Signals.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- A comprehensive survey on diagnosis and assessment of Parkinson's disease via plantar pressure analysis.NPJ Parkinson's disease · 2026Article
- Automatic differentiation of voluntary and tremulous motion using ensemble empirical mode decomposition and convolutional Bi-directional LSTM.Scientific reports · 2025Article
- Enhancing Diagnostic Accuracy of Neurological Disorders Through Feature-Driven Multi-Class Classification with Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- A survey of detection of Parkinson's disease using artificial intelligence models with multiple modalities and various data preprocessing techniques.Journal of education and health promotion · 2024Review
- Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics.PeerJ. Computer science · 2024Article
- Spatial and Temporal Gait Characteristics in Patients Admitted to a Neuro-Rehabilitation Department with Age-Related White Matter Changes: A Gait Analysis and Clinical Study.Neurology international · 2023Article
- Repeatability and Temporal Consistency of Lower Limb Biomechanical Variables Expressing Interlimb Coordination during the Double-Support Phase in People with and without Stroke Sequelae.Sensors (Basel, Switzerland) · 2023Article
- Optimized MobileNetV3: a deep learning-based Parkinson's disease classification using fused images.PeerJ. Computer science · 2023Article
- Early and High-Accuracy Diagnosis of Parkinson's Disease: Outcomes of a New Model.Computational and mathematical methods in medicine · 2023Article
- Automatic Detection of Tuberculosis Using VGG19 with Seagull-Algorithm.Life (Basel, Switzerland) · 2022Article
- A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
- Imperative Role of Machine Learning Algorithm for Detection of Parkinson's Disease: Review, Challenges and Recommendations.Diagnostics (Basel, Switzerland) · 2022Review
- Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson's Disease Affected by COVID-19: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
- Human Activity Recognition Based on Embedded Sensor Data Fusion for the Internet of Healthcare Things.Healthcare (Basel, Switzerland) · 2022Article
- Machine Learning Approach to Support the Detection of Parkinson's Disease in IMU-Based Gait Analysis.Sensors (Basel, Switzerland) · 2022Article
- Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters.Sensors (Basel, Switzerland) · 2022Article
- Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson's Disease: A Narrative Review.Diagnostics (Basel, Switzerland) · 2022Review
- Tuberculosis Detection in Chest Radiographs Using Spotted Hyena Algorithm Optimized Deep and Handcrafted Features.Computational intelligence and neuroscience · 2022Article
- Tooth and Bone Parameters in the Assessment of the Chronological Age of Children and Adolescents Using Neural Modelling Methods.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
6 authors.
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Abstract
Parkinson's disease (PD) is a neuro-degenerative disorder primarily triggered due to the deterioration of dopamine-producing neurons in the substantia nigra of the human brain. The early detection of Parkinson's disease can assist in preventing deteriorating health. This paper analyzes human gait signals using Local Binary Pattern (LBP) techniques during feature extraction before classification. Supplementary to the LBP techniques, Local Gradient Pattern (LGP), Local Neighbour Descriptive Pattern (LNDP), and Local Neighbour Gradient Pattern (LNGP) were utilized to extract features from gait signals. The statistical features were derived and analyzed, and the statistical Kruskal-Wallis test was carried out for the selection of an optimal feature set. The classification was then carried out by an Artificial Neural Network (ANN) for the identified feature set. The proposed Symmetrically Weighted Local Neighbour Gradient Pattern (SWLNGP) method achieves a better performance, with 96.28% accuracy, 96.57% sensitivity, and 95.94% specificity. This study suggests that SWLNGP could be an effective feature extraction technique for the recognition of Parkinsonian gait.
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