Angular Velocity Estimation of Knee Joint Based on MMG Signals

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

doi:10.1007/978-3-030-27532-7_2

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…Sample entropy and Spearman’s correlation coefficients of the MMG signals are selected as other features. 31…”

Section: Experiments and Signal Preprocessingmentioning

confidence: 99%

“…Sample entropy and Spearman's correlation coefficients of the MMG signals are selected as other features. 31 To improve the speed of data processing, the 48-dimensional time and frequency domain feature data constructed from five MMG sensors data are reduced dimensionally by the PCA. To implement the PCA, we call the PCA function in MATLAB 2018A.…”

Section: Feature Extraction and Dimensions Reductionmentioning

confidence: 99%

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

Xie

Wang

et al. 2020

International Journal of Advanced Robotic Systems

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With the growth of the number of elderly and disabled with motor dysfunction, the demand for assisted exercise is increasing. Wearable power assistance robots are developed to provide athletic ability of limbs for the elderly or the disabled who have weakened limbs to better self-care ability. Existing wearable power-assisted robots generally use surface electromyography (sEMG) to obtain effective human motion intentions. Due to the characteristics of sEMG signals, it is limited in many applications. To solve the above problems, we design a long short-term memory (LSTM) neural network model based on human mechanomyography (MMG) signals to estimate the motion acceleration of knee joint. The acceleration can be further calculated by the torque required for movement control of the wearable power assistance robots for the lower limb. We detect MMG signals on the clothed thigh, extract features of the MMG signals, and then, use principal component analysis to reduce the features’ dimensions. Finally, the dimension-reduced features are inputted into the LSTM neural network model in time series for estimating the acceleration. The experimental results show that the average correlation coefficient ( R) is 94.48 ± 1.91% for the estimation of acceleration in the process of continuously performing under approximately π/4 rad/s. This approach can be applied in the practical applications of wearable field.

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“…Sample entropy and Spearman’s correlation coefficients of the MMG signals are selected as other features. 31…”

Section: Experiments and Signal Preprocessingmentioning

confidence: 99%

Section: Feature Extraction and Dimensions Reductionmentioning

confidence: 99%

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

Xie

Wang

et al. 2020

International Journal of Advanced Robotic Systems

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“…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%

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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Angular Velocity Estimation of Knee Joint Based on MMG Signals

Cited by 2 publications

References 19 publications

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

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

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

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