Modeling and Individualizing Continuous Joint Kinematics Using Gaussian Process Enhanced Fourier Series
Prosthetic discrete controller relies on finite state machines to switch between a set of predefined task-specific controllers. Therefore the prosthesis can only perform a limited number of discrete locomotion tasks and need hours to tune the parameters for each user. In contrast, the continuous con...
| 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/9958940/ |
| _version_ | 1797805118905647104 |
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| author | Yongshan Huang Honglei An Hongxu Ma Qing Wei |
| author_facet | Yongshan Huang Honglei An Hongxu Ma Qing Wei |
| author_sort | Yongshan Huang |
| collection | DOAJ |
| description | Prosthetic discrete controller relies on finite state machines to switch between a set of predefined task-specific controllers. Therefore the prosthesis can only perform a limited number of discrete locomotion tasks and need hours to tune the parameters for each user. In contrast, the continuous controller treats a gait cycle in a unified way. Thus it is expected to better facilitate normative biomechanics by providing a gait predictive model to contribute a non-switching controller that supports a continuum of tasks. Furthermore, a better method is to train a personalized trajectory prediction model suitable for personal characteristics according to personal walking data. This paper proposes a Gaussian process enhanced Fourier series (GPEFS) method to construct a gait prediction model that represents the human locomotion as a continuous function of phase, speed and slope. Firstly the joint trajectories are transformed into the Fourier coefficient space by least square method. Then the relationship between each Fourier coefficient and task input can be learned by multiple Gaussian process regression (GPRs) model respectively. Compared with directly using GPR to fit the joint trajectory under multi task, our method greatly reduces the computational burden, so as to meet the real-time application scenario. In addition, in Fourier coefficient space, the difference in all tasks between the Fourier coefficient of personal data and the one of statistical data follows the same trend. Therefore, a personalized prediction model is built to predict an individual’s kinematics over a continuous range of slopes and speeds given only one personalized task at level ground and normal speed. The experimental results show that the gait prediction model and the personalized prediction model are feasible and effective. |
| first_indexed | 2024-03-13T05:46:12Z |
| format | Article |
| id | doaj.art-db49c515334e4dedbb9f4276f38e79d5 |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:46:12Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-db49c515334e4dedbb9f4276f38e79d52023-06-13T20:09:44ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102023-01-013177978810.1109/TNSRE.2022.32239929958940Modeling and Individualizing Continuous Joint Kinematics Using Gaussian Process Enhanced Fourier SeriesYongshan Huang0https://orcid.org/0000-0002-0920-1154Honglei An1Hongxu Ma2https://orcid.org/0000-0002-4662-2986Qing Wei3Robot Research Center, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, ChinaRobot Research Center, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, ChinaRobot Research Center, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, ChinaRobot Research Center, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, ChinaProsthetic discrete controller relies on finite state machines to switch between a set of predefined task-specific controllers. Therefore the prosthesis can only perform a limited number of discrete locomotion tasks and need hours to tune the parameters for each user. In contrast, the continuous controller treats a gait cycle in a unified way. Thus it is expected to better facilitate normative biomechanics by providing a gait predictive model to contribute a non-switching controller that supports a continuum of tasks. Furthermore, a better method is to train a personalized trajectory prediction model suitable for personal characteristics according to personal walking data. This paper proposes a Gaussian process enhanced Fourier series (GPEFS) method to construct a gait prediction model that represents the human locomotion as a continuous function of phase, speed and slope. Firstly the joint trajectories are transformed into the Fourier coefficient space by least square method. Then the relationship between each Fourier coefficient and task input can be learned by multiple Gaussian process regression (GPRs) model respectively. Compared with directly using GPR to fit the joint trajectory under multi task, our method greatly reduces the computational burden, so as to meet the real-time application scenario. In addition, in Fourier coefficient space, the difference in all tasks between the Fourier coefficient of personal data and the one of statistical data follows the same trend. Therefore, a personalized prediction model is built to predict an individual’s kinematics over a continuous range of slopes and speeds given only one personalized task at level ground and normal speed. The experimental results show that the gait prediction model and the personalized prediction model are feasible and effective.https://ieeexplore.ieee.org/document/9958940/Human locomotiongaussian process regressionFourier seriesgait predictive modelsprosthetic limbs |
| spellingShingle | Yongshan Huang Honglei An Hongxu Ma Qing Wei Modeling and Individualizing Continuous Joint Kinematics Using Gaussian Process Enhanced Fourier Series IEEE Transactions on Neural Systems and Rehabilitation Engineering Human locomotion gaussian process regression Fourier series gait predictive models prosthetic limbs |
| title | Modeling and Individualizing Continuous Joint Kinematics Using Gaussian Process Enhanced Fourier Series |
| title_full | Modeling and Individualizing Continuous Joint Kinematics Using Gaussian Process Enhanced Fourier Series |
| title_fullStr | Modeling and Individualizing Continuous Joint Kinematics Using Gaussian Process Enhanced Fourier Series |
| title_full_unstemmed | Modeling and Individualizing Continuous Joint Kinematics Using Gaussian Process Enhanced Fourier Series |
| title_short | Modeling and Individualizing Continuous Joint Kinematics Using Gaussian Process Enhanced Fourier Series |
| title_sort | modeling and individualizing continuous joint kinematics using gaussian process enhanced fourier series |
| topic | Human locomotion gaussian process regression Fourier series gait predictive models prosthetic limbs |
| url | https://ieeexplore.ieee.org/document/9958940/ |
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