Automated Parkinson’s Disease Diagnosis Using Decomposition Techniques and Deep Learning for Accurate Gait Analysis Article Swipe
S. Jeba Priya
,
C. Anand Deva Durai
,
M. S. P. Subathra
,
S. Thomas George
,
Andrew Jeyabose
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3562566
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3562566
Parkinson’s disease (PD) is a prevalent neurological disorder that significantly impacts posture and gait, leading to movement abnormalities due to malfunctions in the brain and nervous system. Gait signals are essential for identifying PD, and various techniques have been employed for classification, with a focus on spatiotemporal factors. Additionally, cognitive monitoring systems for PD symptoms have been developed. Recent advancements involve decomposing gait signals using techniques such as empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) to streamline data for improved computational efficiency. Machine learning (ML) and deep learning (DL) algorithms are widely used to enhance classification accuracy. This study integrates decomposition techniques with ML algorithms such as support vector machines (SVMs), artificial neural networks (ANNs), decision trees (DTs), and k-nearest neighbors (k-NNs), as well as DL algorithms such as long short-term memory (LSTM), bidirectional long short-term memory (LSTM), and convolutional neural networks (CNNs), for PD classification. The combination of VMD with the 1D-CNN achieved the highest accuracy, sensitivity, and specificity, with values of 99.1 %, 100 %, and 100 %, respectively. This finding suggests a promising approach for further research in this field. The optimized VMD-1D-CNN combination demonstrated significant potential for accurately diagnosing PD based on gait dynamics. The successful application of these methods highlights the importance of advanced signal processing techniques in improving the detection and management of neurological disorders.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2025.3562566
- OA Status
- gold
- Cited By
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4409640611Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/access.2025.3562566Digital Object Identifier
- Title
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Automated Parkinson’s Disease Diagnosis Using Decomposition Techniques and Deep Learning for Accurate Gait AnalysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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S. Jeba Priya, C. Anand Deva Durai, M. S. P. Subathra, S. Thomas George, Andrew JeyaboseList of authors in order
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https://doi.org/10.1109/access.2025.3562566Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2025.3562566Direct OA link when available
- Concepts
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Gait analysis, Computer science, Gait, Artificial intelligence, Decomposition, Parkinson's disease, Deep learning, Pattern recognition (psychology), Physical medicine and rehabilitation, Machine learning, Disease, Medicine, Pathology, Ecology, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works_count | 60 |
| abstract_inverted_index.a | 4, 43, 180 |
| abstract_inverted_index.%, | 170, 172, 175 |
| abstract_inverted_index.DL | 131 |
| abstract_inverted_index.ML | 109 |
| abstract_inverted_index.PD | 53, 150, 199 |
| abstract_inverted_index.as | 67, 112, 128, 130, 134 |
| abstract_inverted_index.in | 21, 186, 218 |
| abstract_inverted_index.is | 3 |
| abstract_inverted_index.of | 154, 168, 207, 213, 224 |
| abstract_inverted_index.on | 45, 201 |
| abstract_inverted_index.to | 15, 19, 81, 99 |
| abstract_inverted_index.100 | 171, 174 |
| abstract_inverted_index.PD, | 33 |
| abstract_inverted_index.The | 152, 189, 204 |
| abstract_inverted_index.VMD | 155 |
| abstract_inverted_index.and | 12, 24, 34, 76, 91, 124, 144, 164, 173, 222 |
| abstract_inverted_index.are | 29, 96 |
| abstract_inverted_index.due | 18 |
| abstract_inverted_index.for | 31, 40, 52, 84, 149, 183, 196 |
| abstract_inverted_index.the | 22, 157, 160, 211, 220 |
| abstract_inverted_index.(DL) | 94 |
| abstract_inverted_index.(ML) | 90 |
| abstract_inverted_index.(PD) | 2 |
| abstract_inverted_index.99.1 | 169 |
| abstract_inverted_index.Gait | 27 |
| abstract_inverted_index.This | 103, 177 |
| abstract_inverted_index.been | 38, 56 |
| abstract_inverted_index.data | 83 |
| abstract_inverted_index.deep | 92 |
| abstract_inverted_index.gait | 62, 202 |
| abstract_inverted_index.have | 37, 55 |
| abstract_inverted_index.long | 135, 140 |
| abstract_inverted_index.mode | 69, 78 |
| abstract_inverted_index.such | 66, 111, 133 |
| abstract_inverted_index.that | 8 |
| abstract_inverted_index.this | 187 |
| abstract_inverted_index.used | 98 |
| abstract_inverted_index.well | 129 |
| abstract_inverted_index.with | 42, 108, 156, 166 |
| abstract_inverted_index.(VMD) | 80 |
| abstract_inverted_index.based | 200 |
| abstract_inverted_index.brain | 23 |
| abstract_inverted_index.focus | 44 |
| abstract_inverted_index.gait, | 13 |
| abstract_inverted_index.study | 104 |
| abstract_inverted_index.these | 208 |
| abstract_inverted_index.trees | 122 |
| abstract_inverted_index.using | 64 |
| abstract_inverted_index.(DTs), | 123 |
| abstract_inverted_index.(EMD), | 71 |
| abstract_inverted_index.(EWT), | 75 |
| abstract_inverted_index.1D-CNN | 158 |
| abstract_inverted_index.Recent | 58 |
| abstract_inverted_index.field. | 188 |
| abstract_inverted_index.memory | 137, 142 |
| abstract_inverted_index.neural | 118, 146 |
| abstract_inverted_index.signal | 215 |
| abstract_inverted_index.values | 167 |
| abstract_inverted_index.vector | 114 |
| abstract_inverted_index.widely | 97 |
| abstract_inverted_index.(ANNs), | 120 |
| abstract_inverted_index.(CNNs), | 148 |
| abstract_inverted_index.(LSTM), | 138, 143 |
| abstract_inverted_index.(SVMs), | 116 |
| abstract_inverted_index.Machine | 88 |
| abstract_inverted_index.disease | 1 |
| abstract_inverted_index.enhance | 100 |
| abstract_inverted_index.finding | 178 |
| abstract_inverted_index.further | 184 |
| abstract_inverted_index.highest | 161 |
| abstract_inverted_index.impacts | 10 |
| abstract_inverted_index.involve | 60 |
| abstract_inverted_index.leading | 14 |
| abstract_inverted_index.methods | 209 |
| abstract_inverted_index.nervous | 25 |
| abstract_inverted_index.posture | 11 |
| abstract_inverted_index.signals | 28, 63 |
| abstract_inverted_index.support | 113 |
| abstract_inverted_index.system. | 26 |
| abstract_inverted_index.systems | 51 |
| abstract_inverted_index.various | 35 |
| abstract_inverted_index.wavelet | 73 |
| abstract_inverted_index.(k-NNs), | 127 |
| abstract_inverted_index.achieved | 159 |
| abstract_inverted_index.advanced | 214 |
| abstract_inverted_index.approach | 182 |
| abstract_inverted_index.decision | 121 |
| abstract_inverted_index.disorder | 7 |
| abstract_inverted_index.employed | 39 |
| abstract_inverted_index.factors. | 47 |
| abstract_inverted_index.improved | 85 |
| abstract_inverted_index.learning | 89, 93 |
| abstract_inverted_index.machines | 115 |
| abstract_inverted_index.movement | 16 |
| abstract_inverted_index.networks | 119, 147 |
| abstract_inverted_index.research | 185 |
| abstract_inverted_index.suggests | 179 |
| abstract_inverted_index.symptoms | 54 |
| abstract_inverted_index.accuracy, | 162 |
| abstract_inverted_index.accuracy. | 102 |
| abstract_inverted_index.cognitive | 49 |
| abstract_inverted_index.detection | 221 |
| abstract_inverted_index.dynamics. | 203 |
| abstract_inverted_index.empirical | 68, 72 |
| abstract_inverted_index.essential | 30 |
| abstract_inverted_index.improving | 219 |
| abstract_inverted_index.k-nearest | 125 |
| abstract_inverted_index.neighbors | 126 |
| abstract_inverted_index.optimized | 190 |
| abstract_inverted_index.potential | 195 |
| abstract_inverted_index.prevalent | 5 |
| abstract_inverted_index.promising | 181 |
| abstract_inverted_index.transform | 74 |
| abstract_inverted_index.VMD-1D-CNN | 191 |
| abstract_inverted_index.accurately | 197 |
| abstract_inverted_index.algorithms | 95, 110, 132 |
| abstract_inverted_index.artificial | 117 |
| abstract_inverted_index.developed. | 57 |
| abstract_inverted_index.diagnosing | 198 |
| abstract_inverted_index.disorders. | 226 |
| abstract_inverted_index.highlights | 210 |
| abstract_inverted_index.importance | 212 |
| abstract_inverted_index.integrates | 105 |
| abstract_inverted_index.management | 223 |
| abstract_inverted_index.monitoring | 50 |
| abstract_inverted_index.processing | 216 |
| abstract_inverted_index.short-term | 136, 141 |
| abstract_inverted_index.streamline | 82 |
| abstract_inverted_index.successful | 205 |
| abstract_inverted_index.techniques | 36, 65, 107, 217 |
| abstract_inverted_index.application | 206 |
| abstract_inverted_index.combination | 153, 192 |
| abstract_inverted_index.decomposing | 61 |
| abstract_inverted_index.efficiency. | 87 |
| abstract_inverted_index.identifying | 32 |
| abstract_inverted_index.significant | 194 |
| abstract_inverted_index.variational | 77 |
| abstract_inverted_index.advancements | 59 |
| abstract_inverted_index.demonstrated | 193 |
| abstract_inverted_index.malfunctions | 20 |
| abstract_inverted_index.neurological | 6, 225 |
| abstract_inverted_index.sensitivity, | 163 |
| abstract_inverted_index.specificity, | 165 |
| abstract_inverted_index.Additionally, | 48 |
| abstract_inverted_index.abnormalities | 17 |
| abstract_inverted_index.bidirectional | 139 |
| abstract_inverted_index.computational | 86 |
| abstract_inverted_index.convolutional | 145 |
| abstract_inverted_index.decomposition | 70, 79, 106 |
| abstract_inverted_index.respectively. | 176 |
| abstract_inverted_index.significantly | 9 |
| abstract_inverted_index.classification | 101 |
| abstract_inverted_index.spatiotemporal | 46 |
| abstract_inverted_index.classification, | 41 |
| abstract_inverted_index.classification. | 151 |
| abstract_inverted_index.Parkinson’s | 0 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.96359064 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |