Estimation of Knee Joint Extension Force Using Mechanomyography Based on IGWO-SVR Algorithm

Li, Zebin; Gao, Lifu; Lu, Wei; Wang, Daqing; Xie, Chenlei; Cao, Hui

doi:10.3390/electronics10232972

Cited by 6 publications

(6 citation statements)

References 42 publications

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“…Therefore, an improved Grey Wolf Optimization (IGWO) algorithm is applied to hunt the optimal VMD parameter ½K, α. Based on the previous improved GWO work [29], tent chaotic mapping is further introduced to make the initial population with uniformly distributed diversity and enhance the global convergence speed of the IGWO algorithm. The optimization process of IGWO is led by alpha, beta, and delta wolves.…”

Section: The Basic Methodsmentioning

confidence: 99%

“…To verify the application effect of the proposed method in practical work, MMG signals measured by the acceleration sensor ADXL335 are analyzed in this section. The MMG signal acquisition method refers to the previous work [29]. MMG signal segments are randomly selected from two subjects with different force situations.…”

Section: Application To Mmg Signalsmentioning

confidence: 99%

“…Among these optimization algorithms, the GWO algorithm has stronger competitiveness [28]. Combined with the previous work [29], the improved GWO (IGWO) has a better global optimizationseeking performance. Therefore, in this paper, the improved GWO is used for VMD parameter optimization.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Noise Suppression and Artifact Removal Method of Mechanomyography Based on RLS, IGWO-VMD, and CEEMDAN

Gao

et al. 2022

Journal of Sensors

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Mechanomyography (MMG) signals have extensive applications in muscle function assessment and human intention recognition. However, during signal acquisition, MMG signals are easily contaminated by noise and artifacts, which seriously affects the recognition of their characteristics. To address these issues, a novel noise suppression and artifact removal method based on recursive least square (RLS), improved Gray Wolf Optimizer-optimized variable mode decomposition (IGWO-VMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is proposed. In this paper, the RLS algorithm is first applied to adaptively filter out the power line interference (PLI). Then, IGWO is designed to select the appropriate VMD parameters and use the VMD to decompose the noisy signal into band-limited intrinsic mode functions (BLIMFs). In addition, the BLIMFs are classified into the low-frequency part and high-frequency part according to the given correlation coefficient (CC) threshold value. The effective components of the low-frequency part are identified by the center frequency. Meanwhile, the high-frequency part is decomposed by CEEMDAN, and its effective components are obtained according to the proposed sample entropy threshold range. Finally, the effective components of the low and high-frequency parts are reconstructed to obtain the denoised signal to realize the extraction of useful signals. Simulation experiment results demonstrate that the proposed method outperforms the classical methods and the designed IGWO-VMD method in terms of denoising performance. The effectiveness of the proposed method is verified through the measured MMG signal experiments. The proposed method not only effectively suppresses noise and artifacts but also overcomes the limitations of VMD and CCEMDAN.

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Section: The Basic Methodsmentioning

confidence: 99%

Section: Application To Mmg Signalsmentioning

confidence: 99%

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A Novel Noise Suppression and Artifact Removal Method of Mechanomyography Based on RLS, IGWO-VMD, and CEEMDAN

Gao

et al. 2022

Journal of Sensors

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“…When MMG signals are employed for muscle force estimation, apart from the consideration of cross-talk, a significant technical difficulty is the development of an appropriate muscle force estimation model. In the previous stage, we constructed knee static extension force estimation models using support vector regression (SVR) [30,31] and achieved high accuracy. However, when applying these models to knee dynamic extension force estimation, the results were not satisfactory.…”

Section: Introductionmentioning

confidence: 99%

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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“… Lu et al (2021) directly collected EMG signals of biceps brachii and input them into the Informer model to predict the contraction force at the elbow end. Li et al (2021) further improved the contraction form based on the thinking of upper limb contraction force and explored the estimation method of lower limb extension force with the IGWO-SVR algorithm.…”

Section: Introductionmentioning

confidence: 99%

A comparison of contributions of individual muscle and combination muscles to interaction force prediction using KPCA-DRSN model

Lü

Gao

Cao

et al. 2022

Front. Bioeng. Biotechnol.

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Rapid and accurate prediction of interaction force is an effective way to enhance the compliant control performance. However, whether individual muscles or a combination of muscles is more suitable for interaction force prediction under different contraction tasks is of great importance in the compliant control of the wearable assisted robot. In this article, a novel algorithm that is based on sEMG and KPCA-DRSN is proposed to explore the relationship between interaction force prediction and sEMG signals. Furthermore, the contribution of each muscle to the interaction force is assessed based on the predicted results. First of all, the experimental platform for obtaining the sEMG is described. Then, the raw sEMG signal of different muscles is collected from the upper arm during different contractions. Meanwhile, the output force is collected by the force sensor. The Kernel Principal Component Analysis (KPCA) method is adopted to remove the invalid components of the raw sEMG signal. After that, the processed sequence is fed into the Deep Residual Shrinkage Network (DRSN) to predict the interaction force. Finally, based on the prediction results, the contribution of each sEMG signal from different muscles to the interaction force is evaluated by the mean impact value (MIV) indicator. The experimental results demonstrate that our methods can automatically extract the valid features of sEMG signal and provided fast and efficient prediction. In addition, the single muscle with the largest MIV index could predict the interaction force faster and more accurately than the muscle combination in different contraction tasks. The finding of our research provides a solid evidence base for the compliant control of the wearable robot.

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Estimation of Knee Joint Extension Force Using Mechanomyography Based on IGWO-SVR Algorithm

Cited by 6 publications

References 42 publications

A Novel Noise Suppression and Artifact Removal Method of Mechanomyography Based on RLS, IGWO-VMD, and CEEMDAN

A Novel Noise Suppression and Artifact Removal Method of Mechanomyography Based on RLS, IGWO-VMD, and CEEMDAN

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

A comparison of contributions of individual muscle and combination muscles to interaction force prediction using KPCA-DRSN model

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