Continuous Estimation of Human Knee Joint Angles by Fusing Kinematic and Myoelectric Signals

Abstract

Exoskeleton robot is an essential tool in active rehabilitation training for patients with lower limb motor dysfunctions. Accurate and real-time recognition in human motion intention is a great challenge in exoskeleton robot, which can be implemented by continues estimation of human joint angles. In this study, we innovatively proposed a novel feature-based convolutional neural network-bidirectional long-short term memory network (CNN-BiLSTM) model to predict the knee joint angles more accurately and in real time. We validated our method on a public dataset, including surface electromyography(sEMG) and inertial measurement unit (IMU) data of 10 healthy subjects during normal walking. Initially, features extraction from each modal data achieved feature-level fusion. Then the importance of each sEMG and IMU signal feature for knee joint angle prediction was quantified by ensemble feature scorer (EFS) and the number of features required for prediction while ensuring accuracy was simplified by profile likelihood maximization (PLM) algorithm. Finally, the CNN-BiLSTM model was created by using the determined simplest features to further fuse the spatio-temporal correlation of signals. The results indicated that the EFS and PLM algorithm could remove the feature redundancy perfectly and estimation performance would become better when bi-modal gait data were fused. For the estimation performance, the average root mean square error (RMSE), adjusted R 2 and pearson correlation coefficient (CC) of our algorithm were 4.07, 0.95, and 0.98, respectively, which was better than CNN 、 BiLSTM and other three traditional machine learning methods. In addition, the model test time was 62.47 ± 0.29 ms, which was less than the predicted horizon of 100 ms. The real-time performance and accuracy are satisfactory. Compared with previous works, our method has great advantages in feature selection and model design, which further improves the prediction accuracy. These promising results demonstrate that the proposed method has considerable potential to be applied to exoskeleton robot control.

Continuous Lower Limb Multi‐Intent Prediction for Electromyography‐Driven Intrinsic and Extrinsic Control

“…In addition, we performed more comparisons of our model with other existing methods and/or datasets. [ 21,22,33–36 ] These results are included in Table S2 and S3, Supporting Information. Our model accomplished dynamic intent prediction, classified more states than most previous studies with higher accuracy and F1‐score, and realized precise angle estimation with better MSE and .…”

MMG-Based Knee Dynamic Extension Force Estimation Using Cross-Talk and IGWO-LSTM

“…Over the past decades, a lot of work has been performed by many experts using muscle activity to predict joint angle [3], acceleration [4], torque [5], muscle force [6][7][8][9], fatigue effect [10], etc. In particular, estimating muscle force from muscle activity in these investigations is a challenging task, which has many potential applications such as diagnosis of muscle dysfunction, rehabilitation training, prosthetic assistive devices, etc.…”

Enhanced Gait Phases Recognition by EMG and Kinematics Information Fusion and a Minimal Recording Setuping Setup

“…In pattern recognition, also known as classification, the decoded motor tasks are divided into a finite number of clusters of pre-planned trajectories [6], [8]. Although it's burdened by the incapability to generalize to unseen motor tasks, this approach was largely employed by previous studies, due to its promising performances [9], [18]. On the other hand, regression approaches involve direct decoding of the signal information into continuous output variables, thus being more adaptive to different contexts, providing higher autonomy to the user [8], [18].…”

“…Although it's burdened by the incapability to generalize to unseen motor tasks, this approach was largely employed by previous studies, due to its promising performances [9], [18]. On the other hand, regression approaches involve direct decoding of the signal information into continuous output variables, thus being more adaptive to different contexts, providing higher autonomy to the user [8], [18]. This approach remains challenging in a practical context, due to the high non-linearity between the myoelectric information and the targeted joint angle or torque that limits the prediction accuracy [8].…”