Gaussian Process Model for Touch Probing

2018 ◽  
Author(s):  
Xilu Wang ◽  
Xiaoping Qian
Keyword(s):  
Gaussian Process ◽  
Process Model ◽  
Reconstruction Error ◽  
Gaussian Process Model ◽  
Numerical Examples ◽  
Coordinate Measurement ◽  
Uniform Sampling ◽  
Coordinate Measurement Machine ◽  
Machining Errors ◽  
Surface Deviations

In this paper, we present an approach to determine probing points for the CMM (coordinate measurement machine) measurement. A surface uncertainty based approach is developed to maximize the amount of information acquired by the touch probing of the surface deviations caused by machining errors. The surface uncertainty is modeled by the Gaussian process model and the probing points are selected to minimize the maximum surface uncertainty (surface variance) conditioned on the touch probings at the selected points. The algorithm has been tested with various numerical examples and has been applied in real machining scenarios. The surface reconstruction error based on the developed algorithm is 50% smaller than uniform sampling. The experiments of on machine probing has validated that the selected points can adequately capture the machining errors.

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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