Cargando…
Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals
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...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8175857/ https://www.ncbi.nlm.nih.gov/pubmed/34095080 http://dx.doi.org/10.3389/fpubh.2021.685596 |
_version_ | 1783703136016269312 |
---|---|
author | Liang, Jie Shi, Zhengyi Zhu, Feifei Chen, Wenxin Chen, Xin Li, Yurong |
author_facet | Liang, Jie Shi, Zhengyi Zhu, Feifei Chen, Wenxin Chen, Xin Li, Yurong |
author_sort | Liang, Jie |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8175857 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81758572021-06-05 Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals Liang, Jie Shi, Zhengyi Zhu, Feifei Chen, Wenxin Chen, Xin Li, Yurong Front Public Health Public Health 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. Frontiers Media S.A. 2021-05-21 /pmc/articles/PMC8175857/ /pubmed/34095080 http://dx.doi.org/10.3389/fpubh.2021.685596 Text en Copyright © 2021 Liang, Shi, Zhu, Chen, Chen and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Liang, Jie Shi, Zhengyi Zhu, Feifei Chen, Wenxin Chen, Xin Li, Yurong Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals |
title | Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals |
title_full | Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals |
title_fullStr | Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals |
title_full_unstemmed | Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals |
title_short | Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals |
title_sort | gaussian process autoregression for joint angle prediction based on semg signals |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8175857/ https://www.ncbi.nlm.nih.gov/pubmed/34095080 http://dx.doi.org/10.3389/fpubh.2021.685596 |
work_keys_str_mv | AT liangjie gaussianprocessautoregressionforjointanglepredictionbasedonsemgsignals AT shizhengyi gaussianprocessautoregressionforjointanglepredictionbasedonsemgsignals AT zhufeifei gaussianprocessautoregressionforjointanglepredictionbasedonsemgsignals AT chenwenxin gaussianprocessautoregressionforjointanglepredictionbasedonsemgsignals AT chenxin gaussianprocessautoregressionforjointanglepredictionbasedonsemgsignals AT liyurong gaussianprocessautoregressionforjointanglepredictionbasedonsemgsignals |