Non-parametric warping via local scale estimation for non-stationary Gaussian process modelling

2017 ◽  
Author(s):  
Sébastien Marmin ◽  
Jean Baccou ◽  
Jacques Liandrat ◽  
David Ginsbourger
Keyword(s):  
Gaussian Process ◽  
Local Scale ◽  
Process Modelling ◽  
Stationary Gaussian Process ◽  
Scale Estimation ◽  
Non Parametric

Multi-model multivariate Gaussian process modelling with correlated noises

2017 ◽  
Vol 58 ◽  
pp. 11-22 ◽  
Author(s):  
Xiaodan Hong ◽  
Biao Huang ◽  
Yongsheng Ding ◽  
Fan Guo ◽  
Lei Chen ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Process Modelling ◽  
Multivariate Gaussian ◽  
Multivariate Gaussian Process ◽  
Correlated Noises

scholarly journals Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals

2021 ◽  
Vol 9 ◽  
Author(s):  
Jie Liang ◽  
Zhengyi Shi ◽  
Feifei Zhu ◽  
Wenxin Chen ◽  
Xin Chen ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Probabilistic Model ◽  
Autoregressive Model ◽  
Process Model ◽  
Joint Angle ◽  
Gaussian Process Model ◽  
The Mean ◽  
Test Scenarios ◽  
Semg Signals ◽  
Non Parametric

There is uncertainty in the neuromusculoskeletal system, and deterministic models cannot describe this significant presence of uncertainty, affecting the accuracy of model predictions. In this paper, a knee joint angle prediction model based on surface electromyography (sEMG) signals is proposed. To address the instability of EMG signals and the uncertainty of the neuromusculoskeletal system, a non-parametric probabilistic model is developed using a Gaussian process model combined with the physiological properties of muscle activation. Since the neuromusculoskeletal system is a dynamic system, the Gaussian process model is further combined with a non-linear autoregressive with eXogenous inputs (NARX) model to create a Gaussian process autoregression model. In this paper, the normalized root mean square error (NRMSE) and the correlation coefficient (CC) are compared between the joint angle prediction results of the Gaussian process autoregressive model prediction and the actual joint angle under three test scenarios: speed-dependent, multi-speed and speed-independent. The mean of NRMSE and the mean of CC for all test scenarios in the healthy subjects dataset and the hemiplegic patients dataset outperform the results of the Gaussian process model, with significant differences (p < 0.05 and p < 0.05, p < 0.05 and p < 0.05). From the perspective of uncertainty, a non-parametric probabilistic model for joint angle prediction is established by using Gaussian process autoregressive model to achieve accurate prediction of human movement.

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