Modeling and Individualizing Continuous Joint Kinematics Using Gaussian Process Enhanced Fourier Series

作者：Yongshan Huang, Honglei An, Hongxu Ma, Qing Wei · 发表于：IEEE Transactions on Neural Systems and Rehabilitation Engineering · 年份：2022 · DOI：10.1109/tnsre.2022.3223992 · 被引用次数：13 · 研究领域：Prosthetics and Rehabilitation Robotics、Muscle activation and electromyography studies、Biomedical and Engineering Education

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...