Abstract
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.
Highlights
In human-machine interaction (HMI), surface electromyography is often used to serve as the input signal source. e sEMG signal is a weak electrical potential generated by muscle cells upon electrical or neurological activation, and it is detected from superficial muscles by using surface electrodes. e signal contains abundant information and has a distinct characteristic
To address the above challenges, this study developed a new model that fuses sEMG and MMG signals and brings together Hill-based muscle model (HMM) and joint dynamics. is model can directly calculate the joint motion from sEMG and MMG signals
E identified parameters were substituted into the surface electromyography- (sEMG-)MMG state-space model above to directly estimate the joint angle through the sEMG-MMG features and closed-loop prediction approaches
Summary
In human-machine interaction (HMI), surface electromyography (sEMG) is often used to serve as the input signal source. e sEMG signal is a weak electrical potential generated by muscle cells upon electrical or neurological activation, and it is detected from superficial muscles by using surface electrodes. e signal contains abundant information and has a distinct characteristic. In human-machine interaction (HMI), surface electromyography (sEMG) is often used to serve as the input signal source. E sEMG signal has become a research hotspot in the field of human-computer interaction technology, especially in the manufacturing of exoskeleton robots [1], intelligent prosthetics [2], and rehabilitation robots [3]. SEMG studies usually focus on feature extraction and pattern classification. Studies on the classification of discrete actions have been done from the laboratory to the market [5]. Estimating continuous human joint movement is a critical focus in sEMG at present. Many achievements have already been made, especially in the field of rehabilitation robots, where forecasting the continuous motion variables of patients is vital to achieve smooth control of the rehabilitation robot [6]. The sEMG signal is susceptible to interference from sweat and skin impedance, which deteriorates the control accuracy
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