Abstract
EMG-based motion estimation is required for applications such as myoelectric control, where the simultaneous estimation of kinematic information, namely joint angle and velocity, is challenging and critical. We propose a novel method for accurately modelling the generated joint angle and velocity simultaneously under isotonic, isokinetic (quasi-dynamic), and fully dynamic conditions. Our solution uses two streams of CNN, called TS-CNN to learn informative features from raw EMG data using different scales and estimate the generated motion during elbow flexion and extension. The experimental results show the robustness of our approach in comparison to conventional CNN as well as some methods used in the literature. The best obtained R2 values, are 0.81±0.06, 0.71±0.06, and 0.80±0.13 for joint angle estimation and 0.78±0.05, 0.79±0.07, and 0.71±0.13 for the velocity estimation, during isotonic, isokinetic, and dynamic contractions, respectively. Additionally, our results indicate that the experimental condition can have an impact on the model's performance for motion prediction. EMG-based velocity estimation obtains higher performance than joint angle estimation under isokinetic conditions. Under dynamic conditions, joint angle estimation is more accurate than velocity estimation, and there is no difference between joint angle and velocity estimation in the isotonic case.
Highlights
Upper limb movements are essential for activities of daily living, and the electromyogram (EMG) has been used to estimate upper limb kinematics [1]–[5]
The main contributions of this work can be summarized as follows: (1) We propose a new solution, TS-convolutional neural network (CNN) which employs two CNN streams with different filter sizes to estimate kinematic information from HD-EMG signals
Where f is the function which describes the process of using EMG recording from the input layer, extracting necessary features which are fed to the last layer, where the output is generated. θ represents the parameters of the two-stream CNN (TS-CNN) model
Summary
Upper limb movements are essential for activities of daily living, and the electromyogram (EMG) has been used to estimate upper limb kinematics [1]–[5]. EMG-based human motion intention prediction studies can be grouped into classification and regression models. Regression models focus on continuous upper limb kinematics estimation [1]–[5], [14]–[16]. In the regression models, depending on the application, the estimated motor intent from the EMG during muscle contractions will be mapped to the generated force/torque [14]–[18], velocity [19], or position/angular displacement [2], [5]. We will focus on the EMG-based estimation of continuous human upper limb motion, velocity and joint angle estimation
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