Model of Gait Control in Parkinson’s Disease and Prediction of Robotic Assistance
Gait variability of healthy adults exhibits Long-Range Autocorrelations (LRA), meaning that the stride interval at any time statistically depends on previous gait cycles; and this dependency spans over several hundreds of strides. Previous works have shown that this property is altered in patients w...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10044709/ |
| _version_ | 1797805121384480768 |
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| author | Clemence Vandamme Virginie Otlet Renaud Ronsse Frederic Crevecoeur |
| author_facet | Clemence Vandamme Virginie Otlet Renaud Ronsse Frederic Crevecoeur |
| author_sort | Clemence Vandamme |
| collection | DOAJ |
| description | Gait variability of healthy adults exhibits Long-Range Autocorrelations (LRA), meaning that the stride interval at any time statistically depends on previous gait cycles; and this dependency spans over several hundreds of strides. Previous works have shown that this property is altered in patients with Parkinson’s disease, such that their gait pattern corresponds to a more random process. Here, we adapted a model of gait control to interpret the reduction in LRA that characterized patients in a computational framework. Gait regulation was modeled as a Linear-Quadratic-Gaussian control problem where the objective was to maintain a fixed velocity through the coordinated regulation of stride duration and length. This objective offers a degree of redundancy in the way the controller can maintain a given velocity, resulting in the emergence of LRA. In this framework, the model suggested that patients exploited less the task redundancy, likely to compensate for an increased stride-to-stride variability. Furthermore, we used this model to predict the potential benefit of an active orthosis on the gait pattern of patients. The orthosis was embedded in the model as a low-pass filter on the series of stride parameters. We show in simulations that, with a suitable level of assistance, the orthosis could help patients recovering a gait pattern with LRA comparable to that of healthy controls. Assuming that the presence of LRA in a stride series is a marker of healthy gait control, our study provides a rationale for developing gait assistance technology to reduce the fall risk associated with Parkinson’s disease. |
| first_indexed | 2024-03-13T05:46:16Z |
| format | Article |
| id | doaj.art-e3d4fc5cca864aec8e0daa1f12af8c49 |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:46:16Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-e3d4fc5cca864aec8e0daa1f12af8c492023-06-13T20:09:58ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102023-01-01311374138310.1109/TNSRE.2023.324528610044709Model of Gait Control in Parkinson’s Disease and Prediction of Robotic AssistanceClemence Vandamme0https://orcid.org/0000-0002-2116-9310Virginie Otlet1https://orcid.org/0000-0001-7097-0369Renaud Ronsse2https://orcid.org/0000-0003-0823-9633Frederic Crevecoeur3https://orcid.org/0000-0002-1147-1153Institute of Information and Communication Technologies, Electronics and Applied Mathematics, UCLouvain, Louvain-la-Neuve, BelgiumLouvain Bionics, Institute of Mechanics, Materials, and Civil Engineering, UCLouvain, Louvain-la-Neuve, BelgiumLouvain Bionics, Institute of Mechanics, Materials, and Civil Engineering, UCLouvain, Louvain-la-Neuve, BelgiumInstitute of Information and Communication Technologies, Electronics and Applied Mathematics, UCLouvain, Louvain-la-Neuve, BelgiumGait variability of healthy adults exhibits Long-Range Autocorrelations (LRA), meaning that the stride interval at any time statistically depends on previous gait cycles; and this dependency spans over several hundreds of strides. Previous works have shown that this property is altered in patients with Parkinson’s disease, such that their gait pattern corresponds to a more random process. Here, we adapted a model of gait control to interpret the reduction in LRA that characterized patients in a computational framework. Gait regulation was modeled as a Linear-Quadratic-Gaussian control problem where the objective was to maintain a fixed velocity through the coordinated regulation of stride duration and length. This objective offers a degree of redundancy in the way the controller can maintain a given velocity, resulting in the emergence of LRA. In this framework, the model suggested that patients exploited less the task redundancy, likely to compensate for an increased stride-to-stride variability. Furthermore, we used this model to predict the potential benefit of an active orthosis on the gait pattern of patients. The orthosis was embedded in the model as a low-pass filter on the series of stride parameters. We show in simulations that, with a suitable level of assistance, the orthosis could help patients recovering a gait pattern with LRA comparable to that of healthy controls. Assuming that the presence of LRA in a stride series is a marker of healthy gait control, our study provides a rationale for developing gait assistance technology to reduce the fall risk associated with Parkinson’s disease.https://ieeexplore.ieee.org/document/10044709/Fractalgait analysisoptimal controlParkinson’s diseaserobotic assistance |
| spellingShingle | Clemence Vandamme Virginie Otlet Renaud Ronsse Frederic Crevecoeur Model of Gait Control in Parkinson’s Disease and Prediction of Robotic Assistance IEEE Transactions on Neural Systems and Rehabilitation Engineering Fractal gait analysis optimal control Parkinson’s disease robotic assistance |
| title | Model of Gait Control in Parkinson’s Disease and Prediction of Robotic Assistance |
| title_full | Model of Gait Control in Parkinson’s Disease and Prediction of Robotic Assistance |
| title_fullStr | Model of Gait Control in Parkinson’s Disease and Prediction of Robotic Assistance |
| title_full_unstemmed | Model of Gait Control in Parkinson’s Disease and Prediction of Robotic Assistance |
| title_short | Model of Gait Control in Parkinson’s Disease and Prediction of Robotic Assistance |
| title_sort | model of gait control in parkinson x2019 s disease and prediction of robotic assistance |
| topic | Fractal gait analysis optimal control Parkinson’s disease robotic assistance |
| url | https://ieeexplore.ieee.org/document/10044709/ |
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