scholarly journals sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xugang Xi ◽  
Chen Yang ◽  
Seyed M. Miran ◽  
Yun-Bo Zhao ◽  
Shuliang Lin ◽  
...  
Keyword(s):  
State Space ◽  
Particle Filtering ◽  
Joint Angle ◽  
State Space Model ◽  
Joint Torque ◽  
Permutation Entropy ◽  
Extended Model ◽  
Space Model ◽  
Multijoint Movements ◽  
Continuous Estimation

Continuous joint angle estimation plays an important role in motion intention recognition and rehabilitation training. In this study, a surface electromyography- (sEMG-) mechanomyography (MMG) state-space model is proposed to estimate continuous multijoint movements from sEMG and MMG signals accurately. The model combines forward dynamics with a Hill-based muscle model that estimates joint torque only in a nonfeedback form, making the extended model capable of predicting the multijoint motion directly. The sEMG and MMG features, including the Wilson amplitude and permutation entropy, are then extracted to construct a measurement equation to reduce system error and external disturbances. Using the proposed model, a closed-loop prediction-correction approach, unscented particle filtering, is used to estimate the joint angle from sEMG and MMG signals. Comprehensive experiments are conducted on the human elbow and shoulder joint, and remarkable improvements are demonstrated compared with conventional methods.

scholarly journals Particle Filtering: A Priori Estimation of Observational Errors of a State-Space Model with Linear Observation Equation

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1445
Author(s):  
Rodi Lykou ◽  
George Tsaklidis
Keyword(s):  
State Space ◽  
Particle Filtering ◽  
A Priori ◽  
State Space Model ◽  
Computational Cost ◽  
The State ◽  
Observation Equation ◽  
Basic Algorithm ◽  
Space Model ◽  
Observational Errors

Observational errors of Particle Filtering are studied over the case of a state-space model with a linear observation equation. In this study, the observational errors are estimated prior to the upcoming observations. This action is added to the basic algorithm of the filter as a new step for the acquisition of the state estimations. This intervention is useful in the presence of missing data problems mainly, as well as sample tracking for impoverishment issues. It applies theory of Homogeneous and Non-Homogeneous closed Markov Systems to the study of particle distribution over the state domain and, thus, lays the foundations for the employment of stochastic control against impoverishment. A simulating example is quoted to demonstrate the effectiveness of the proposed method in comparison with existing ones, showing that the proposed method is able to combine satisfactory precision of results with a low computational cost and provide an example to achieve impoverishment prediction and tracking.

scholarly journals Method for the single-channel blind separation based on particle filtering

2021 ◽  
pp. 22-26
Author(s):  
Vasyl Semenov ◽  
Evgeniya Semenova
Keyword(s):  
State Space ◽  
Particle Filtering ◽  
Single Channel ◽  
State Space Model ◽  
Separation Method ◽  
Blind Separation ◽  
Digital Signals ◽  
Space Model

A method for single-channel blind separation of two digital signals is proposed. The method is based on particle filtering and state space model of the mixture of two signals. The effectiveness of method is verified and the advantage over the iterative Bayesian separation method is shown.

scholarly journals Gaussian Process-Integrated State Space Model for Continuous Joint Angle Prediction from EMG and Interactive Force in a Human-Exoskeleton System

2019 ◽  
Vol 9 (8) ◽  
pp. 1711 ◽  
Author(s):  
Yan Zeng ◽  
Jiantao Yang ◽  
Yuehong Yin
Keyword(s):  
Gaussian Process ◽  
State Space ◽  
Muscle Activation ◽  
Joint Angle ◽  
State Space Model ◽  
Measurement Model ◽  
The State ◽  
State Model ◽  
Space Model ◽  
Emg Signal

As one of the most direct indicators of the transparency between a human and an exoskeleton, interactive force has rarely been fused with electromyography (EMG) in the control of human-exoskeleton systems, the performances of which are largely determined by the accuracy of the continuous joint angle prediction. To achieve intuitive and naturalistic human intent learning, a state space model (SSM) for continuous angle prediction of knee joint is developed. When the influence of the interactive force is often ignored in the existing models of human-exoskeleton systems, interactive force is applied as the measurement model output of the proposed SSM, and the EMG signal is used as the state model input signal to indicate muscle activation. The forward dynamics of joint motion and the human-machine interaction mechanism, i.e., the biomechanical interpretations of the interactive force generation mechanism, are derived as the bases for the state model and measurement model based on Hill’s muscle model and semiphenomenological (SP) muscular model, respectively. Gaussian process (GP)-based nonlinear autoregressive with the exogenous inputs (NARX) model and back-propagation neural network (BPNN) are applied to provide better adaptivity for the SSM in practical applications. Corresponding experimental results demonstrate the validity and superiority of the method.

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