ArticleFrontiers in public health2021
Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals.
Article in Frontiers in public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Multijoint Continuous Motion Estimation for Human Lower Limb Based on Surface Electromyography.Sensors (Basel, Switzerland) · 2025Article
- An identification method of human joint interaction torque based on discrete EMG signals.Frontiers in bioengineering and biotechnology · 2025Article
- Prediction of Joint Angles Based on Human Lower Limb Surface Electromyography.Sensors (Basel, Switzerland) · 2023Article
- Neuromusculoskeletal model-informed machine learning-based control of a knee exoskeleton with uncertainties quantification.Frontiers in neuroscience · 2023Article
- Deep-Learning to Map a Benchmark Dataset of Non-amputee Ambulation for Controlling an Open Source Bionic Leg.IEEE robotics and automation letters · 2022Article
- Prediction of Biceps Muscle Electromyogram Signal Using a NARX Neural Network.Journal of medical signals and sensorsArticle
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Authors and funding
6 authors.
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Abstract
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 (
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