Impact of Load Variation on Joint Angle Estimation From Surface EMG Signals

Abstract

Many studies use surface electromyogram (sEMG) signals to estimate the joint angle, for control of upper-limb exoskeletons and prostheses. However, several practical factors still affect its clinical applicability. One of these factors is the load variation during daily use. This paper demonstrates that the load variation can have a substantial impact on performance of elbow angle estimation. This impact leads an increase in mean RMSE (Root-Mean-Square Error) from 7.86 to 20.44 in our experimental test. Therefore, we propose three methods to address this issue: 1) pooling the training data from all loads together to form the pooled training data for the training model; 2) adding the measured load value (force sensor) as an additional input; and 3) developing a two-step hybrid estimation approach based on load and sEMG. Experiments are conducted with five subjects to investigate the feasibility of the proposed three methods. The results show that the mean RMSE is reduced from 20.44 to 13.54 using method one, 10.47 using method two, and 8.48 using method three, respectively. Our study indicates that 1) the proposed methods can improve performance and stability on joint angle estimation and 2) sensor fusion (sEMG sensor and force sensor) is an efficient way to resolve the adverse effect of load variation.

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

“…There might also be other possible reasons that contributed to the improvements. One reason was that the addition of interactive force in our model would improve the performance of joint angle prediction [30]. The use of nonredundant and redundant sub-vectors of EMG signals were innovative in their study, but the information acquired only from the EMG signals was still limited.…”

“…Meanwhile, interactive force, which is a direct end-effector reflection in human-exoskeleton systems, can be regarded as an important indicator of transparency between a human and robot [28,29]. Although the fusion of EMG and force would improve the performance of joint angle prediction, interactive force has rarely been used in combination with EMG signals in joint angle prediction [30]. More importantly, how the interactive force works in the human-exoskeleton system model, i.e., its biomechanical generation mechanism, is often ignored.…”

Evaluate the Medial Muscle Strength by Kick Training between the Standing and Sitting Postures

“…This platform adopted the ATmega series, which is equipped with a multi-channel Analog-to-Digital Converter (ADC) with a 10-bit resolution conversion voltage value, combined with the EMG signal sensing module to convert the voltage, displayed with 0-1023 integer. After referring to the sampling frequency of EMG myoelectric signal frequency band that concentrated at 20-400 Hz [20], the sampling frequency of this study was set to 1 kHz in order to meet the data acquisition requirements.…”

An Advanced Adaptive Control of Lower Limb Rehabilitation Robot

“…For example, the skeletal muscle model is used to predict the multi-joint angle, but it is not suitable for interaction control of the human-robot system since the model has many unknown parameters and low accuracy (Buchanan et al, 2004;Meng et al, 2015). The musculoskeletal model is simplified in some researches, for example, joint-angle model was established by introducing the muscle activity and time domain features (Koo and Mak, 2005); the k-order dynamic model was designed by using the LS-SVR method to predict the joint angle (Tang et al, 2016). By establishing the regression model between sEMG and joint angles, the prediction accuracy is significantly improved, but the modeling takes long time, which may cause patient muscle tired.…”