A long short-term memory neural network model for knee joint acceleration estimation using mechanomyography signals

Xie, Chenlei; Wang, Daqing; Wu, Huijun; Gao, Lifu

doi:10.1177/1729881420968702

Cited by 12 publications

(6 citation statements)

References 26 publications

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“…Although previous studies have confirmed that end-to-end deep learning models can extract representative features from sEMG signals [40][41][42], after all, deep learning models are like a black box with poor interpretability. In our experiment, we therefore first manually selected a large number of features, filtered them through EFS, and then fed them into a deep learning model for training.…”

Section: Discussionmentioning

confidence: 93%

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

Sun

Cao

Chen

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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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.

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Section: Discussionmentioning

confidence: 93%

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

Sun

Cao

Chen

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, MMG can also provide important information on muscle activity and motor unit recruitment patterns [16], which can be detected on the skin surface above the target muscle by accelerometers, piezoelectric microphones, etc. [1,4,13,17]. Even though MMG is influenced by many factors such as muscle morphology and the physical environment [18], it has significant advantages over sEMG with no skin preparation, negligible skin impedance, no need for precise test positioning, and less electronic noise interference [13].…”

Section: Introductionmentioning

confidence: 99%

“…Even though MMG is influenced by many factors such as muscle morphology and the physical environment [18], it has significant advantages over sEMG with no skin preparation, negligible skin impedance, no need for precise test positioning, and less electronic noise interference [13]. Notably, MMG has been applied to human motion recognition and kinetic parameter estimation [1,[4][5][6]13,[18][19][20][21]. However, it is still a challenging issue to establish direct relationships between MMG signals and interactive forces due to the complexity and variety of muscle motor unit (MU) recruitment.…”

Section: Introductionmentioning

confidence: 99%

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

Li,

Gao,

Zhang

et al. 2024

Bioengineering

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Mechanomyography (MMG) is an important muscle physiological activity signal that can reflect the amount of motor units recruited as well as the contraction frequency. As a result, MMG can be utilized to estimate the force produced by skeletal muscle. However, cross-talk and time-series correlation severely affect MMG signal recognition in the real world. These restrict the accuracy of dynamic muscle force estimation and their interaction ability in wearable devices. To address these issues, a hypothesis that the accuracy of knee dynamic extension force estimation can be improved by using MMG signals from a single muscle with less cross-talk is first proposed. The hypothesis is then confirmed using the estimation results from different muscle signal feature combinations. Finally, a novel model (improved grey wolf optimizer optimized long short-term memory networks, i.e., IGWO-LSTM) is proposed for further improving the performance of knee dynamic extension force estimation. The experimental results demonstrate that MMG signals from a single muscle with less cross-talk have a superior ability to estimate dynamic knee extension force. In addition, the proposed IGWO-LSTM provides the best performance metrics in comparison to other state-of-the-art models. Our research is expected to not only improve the understanding of the mechanisms of quadriceps contraction but also enhance the flexibility and interaction capabilities of future rehabilitation and assistive devices.

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“…In [14], a generalized three domains fuzzy wavelet neural network (TDFWNN) algorithm was used to accurately estimate the force from an EMG signal without prior knowledge of the biomechanical model. In [15], taking the quadriceps femoris muscle as an example, the acceleration of the knee joint was estimated by using the long short-term memory (LSTM) neural network model. In [16], the Fuzzy Theory and Deep Learning model based on EMG signals was proposed to estimate the applied forces in robotic surgery.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Long-Term Elbow Flexion Force Intervals Based on the Informer Model and Electromyography

Gao

et al. 2021

Electronics

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Accurate and long-term prediction of elbow flexion force can be used to recognize the intended movement and help wearable power-assisted robots to improve control performance. Our study aimed to find a proper relationship between electromyography and flexion force. However, the existing methods must incorporate biomechanical models to produce accurate and timely predictions of flexion force. Elbow flexion force is largely determined by the contractile properties of muscles, and the relationship between flexion force and the motor function of muscles has to be thoroughly analyzed. Therefore, based on the investigation on the contributions of different muscles to the flexion force, original electromyography signals were decomposed into non-linear and non-stationary parts. We selected the mean absolute value (MAV) of the non-linear part and the variance of the non-stationary part as inputs for an Informer prediction model that does not require detailed a priori knowledge of biomechanical models and is optimized for processing time sequences. Finally, a long-term flexion force probability interval is proposed. The proposed framework performs well in predicting long-term flexion force and outperforms other state-of-the-art models when compared to experimental results.

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A long short-term memory neural network model for knee joint acceleration estimation using mechanomyography signals

Cited by 12 publications

References 26 publications

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

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

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

Prediction of Long-Term Elbow Flexion Force Intervals Based on the Informer Model and Electromyography

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