DiffuseGaitNet: Improving Parkinson’s Disease Gait Severity Assessment With a Diffusion Model Framework
Assessing the severity of gait impairment in Parkinson’s disease (PD) using the Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) is typically performed by clinical experts, but this process is time-consuming, subjective, and costly. To...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/11080070/ |
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| author | Arshak Rezvani Nasrin Ravansalar Mohammad Ali Akhaee Andrew J. Greenshaw Russell Greiner Maryam S. Mirian Muhammad Yousefnezhad Martin J. McKeown |
| author_facet | Arshak Rezvani Nasrin Ravansalar Mohammad Ali Akhaee Andrew J. Greenshaw Russell Greiner Maryam S. Mirian Muhammad Yousefnezhad Martin J. McKeown |
| author_sort | Arshak Rezvani |
| collection | DOAJ |
| description | Assessing the severity of gait impairment in Parkinson’s disease (PD) using the Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) is typically performed by clinical experts, but this process is time-consuming, subjective, and costly. To address these challenges, we propose a Guided Diffusion Model with an encoder-only transformer that automatically predicts gait severity by learning the underlying distribution of PD gait and leveraging domain knowledge critical for clinical evaluations. Our diffusion model enables us to generate synthetic PD gait video frames conditioned on clinical features determined by experts to assess disease severity. These synthetic samples contain novel movement patterns not present in the observed data; systems trained on this information have better prediction performance. In addition, we propose a novel classification algorithm that can learn a predictive model, from both observed training data and synthetic samples, to accurately assess PD severity. We evaluate the effectiveness of the proposed method using two human motion datasets across two tasks: PD severity prediction and action classification. Our approach in predicting PD, and our action classification is sufficiently accurate that it can be applied to general applications with healthy subjects performing similar tasks. The full codebase is available on GitHub: <uri>https://github.com/arshakRz/DiffuseGaitNet</uri> |
| format | Article |
| id | doaj-art-aa3c7b78b0c9419085d39db788c846df |
| institution | Kabale University |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-aa3c7b78b0c9419085d39db788c846df2025-08-20T03:31:55ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-01332858286910.1109/TNSRE.2025.358907411080070DiffuseGaitNet: Improving Parkinson’s Disease Gait Severity Assessment With a Diffusion Model FrameworkArshak Rezvani0https://orcid.org/0009-0008-4225-016XNasrin Ravansalar1https://orcid.org/0009-0009-8541-5670Mohammad Ali Akhaee2https://orcid.org/0000-0003-3753-5618Andrew J. Greenshaw3Russell Greiner4https://orcid.org/0000-0001-8327-934XMaryam S. Mirian5https://orcid.org/0000-0001-5411-9902Muhammad Yousefnezhad6https://orcid.org/0000-0002-9402-5027Martin J. McKeown7https://orcid.org/0000-0002-4048-0817Department of Medicine, The University of British Columbia (UBC), Vancouver, CanadaElectrical and Computer Engineering (ECE) Department, University of Tehran, Tehran, IranElectrical and Computer Engineering (ECE) Department, University of Tehran, Tehran, IranDepartment of Psychiatry, University of Alberta (UoA), Edmonton, CanadaDepartment of Computing Science, UoA, Edmonton, CanadaDepartment of Medicine, The University of British Columbia (UBC), Vancouver, CanadaDepartment of Computing Science and the Department of Psychiatry, UoA, Edmonton, CanadaDepartment of Medicine, The University of British Columbia (UBC), Vancouver, CanadaAssessing the severity of gait impairment in Parkinson’s disease (PD) using the Movement Disorder Society’s Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) is typically performed by clinical experts, but this process is time-consuming, subjective, and costly. To address these challenges, we propose a Guided Diffusion Model with an encoder-only transformer that automatically predicts gait severity by learning the underlying distribution of PD gait and leveraging domain knowledge critical for clinical evaluations. Our diffusion model enables us to generate synthetic PD gait video frames conditioned on clinical features determined by experts to assess disease severity. These synthetic samples contain novel movement patterns not present in the observed data; systems trained on this information have better prediction performance. In addition, we propose a novel classification algorithm that can learn a predictive model, from both observed training data and synthetic samples, to accurately assess PD severity. We evaluate the effectiveness of the proposed method using two human motion datasets across two tasks: PD severity prediction and action classification. Our approach in predicting PD, and our action classification is sufficiently accurate that it can be applied to general applications with healthy subjects performing similar tasks. The full codebase is available on GitHub: <uri>https://github.com/arshakRz/DiffuseGaitNet</uri>https://ieeexplore.ieee.org/document/11080070/Diffusion modelsgenerative AItransformersattention-based networksParkinson’s diseasegait impairments |
| spellingShingle | Arshak Rezvani Nasrin Ravansalar Mohammad Ali Akhaee Andrew J. Greenshaw Russell Greiner Maryam S. Mirian Muhammad Yousefnezhad Martin J. McKeown DiffuseGaitNet: Improving Parkinson’s Disease Gait Severity Assessment With a Diffusion Model Framework IEEE Transactions on Neural Systems and Rehabilitation Engineering Diffusion models generative AI transformers attention-based networks Parkinson’s disease gait impairments |
| title | DiffuseGaitNet: Improving Parkinson’s Disease Gait Severity Assessment With a Diffusion Model Framework |
| title_full | DiffuseGaitNet: Improving Parkinson’s Disease Gait Severity Assessment With a Diffusion Model Framework |
| title_fullStr | DiffuseGaitNet: Improving Parkinson’s Disease Gait Severity Assessment With a Diffusion Model Framework |
| title_full_unstemmed | DiffuseGaitNet: Improving Parkinson’s Disease Gait Severity Assessment With a Diffusion Model Framework |
| title_short | DiffuseGaitNet: Improving Parkinson’s Disease Gait Severity Assessment With a Diffusion Model Framework |
| title_sort | diffusegaitnet improving parkinson x2019 s disease gait severity assessment with a diffusion model framework |
| topic | Diffusion models generative AI transformers attention-based networks Parkinson’s disease gait impairments |
| url | https://ieeexplore.ieee.org/document/11080070/ |
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