Simultaneous estimation of grip force and wrist angles by surface electromyography and acceleration signals

Mao, He; Zheng, Yue; Ma, Chunsheng; Wu, Kai; Li, Guanglin; Peng, Fang

doi:10.1016/j.bspc.2022.104088

Cited by 15 publications

(12 citation statements)

References 37 publications

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“…b) Machine Learning: Studies [158]- [160] deployed SVR based on TD, FD, and TFD features for joint angle and grip force predictions. Study [161] utilized TD features alongside Gradient Boosted Regression Trees (GBRT) built on cascaded decision trees to predict joint angles with the generalizability to untrained new data.…”

Section: ) Hand-wrist Jointsmentioning

confidence: 99%

“…Therefore, HD-sEMG exhibits inherent advantages over sEMG in extracting MU features and predictive performance. Regarding multi-sensor fusion, as discussed in studies [21], [55], [79], [114], [140], [151], [160], integrating sEMG sensors with EEG, IMU, FMG, and MMG sensors can further enhance the performance and robustness, especially in scenarios of isometric contractions and under external force interference.…”

Section: A Significant Findings 1) Advantages Of Hd-semg Sensors and ...mentioning

confidence: 99%

See 1 more Smart Citation

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches

Wei,

Zhang,

Xie

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Upper limb functional impairments persisting after stroke significantly affect patients' quality of life. Precise adjustment of robotic assistance levels based on patients' motion intention using sEMG signals is crucial for active rehabilitation. This paper systematically reviews studies over the past decade on continuous prediction of upper limb single joint and multi-joint combinations motion intention using Model-Based (MB) and Model-Free (MF) approaches, based on 186 relevant studies screened from six major electronic databases. The findings indicate ongoing challenges in terms of subject composition, algorithm robustness and generalization, and feasibility for practical applications. Moreover, it suggests integrating the strengths of both MB and MF approaches to improve existing algorithms. Therefore, future research should further explore personalized MB-MF combination methods incorporating deep learning, attention mechanisms, muscle synergy features, motor unit features, and closed-loop feedback to achieve precise, real-time, and long-duration prediction of multi-joint complex movements, while further refining the transfer learning strategy for rapid algorithm deployment across days and subjects. In summary, this review summarizes the current state and challenges of research, aiming to provide inspiration for future research on predicting upper limb motion intention based on sEMG.

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Section: ) Hand-wrist Jointsmentioning

confidence: 99%

Section: A Significant Findings 1) Advantages Of Hd-semg Sensors and ...mentioning

confidence: 99%

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches

Wei,

Zhang,

Xie

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The regression approach can be extended to include other non-EMG signals. In [22], Mao et al proposed a new control scheme that simultaneously estimates continuous grip force and wrist angles using a combination of EMG and acceleration signals. They trained and compared several non-linear regressors based on the SVM classifier using four different EMG features sets in combination with accelerated signals in nine intact subjects.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous Three-Degrees-of-Freedom Prosthetic Control Based on Linear Regression and Closed-Loop Training Protocol

Igual,

Igual

2024

Sensors

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Machine learning-based controllers of prostheses using electromyographic signals have become very popular in the last decade. The regression approach allows a simultaneous and proportional control of the intended movement in a more natural way than the classification approach, where the number of movements is discrete by definition. However, it is not common to find regression-based controllers working for more than two degrees of freedom at the same time. In this paper, we present the application of the adaptive linear regressor in a relatively low-dimensional feature space with only eight sensors to the problem of a simultaneous and proportional control of three degrees of freedom (left–right, up–down and open–close hand movements). We show that a key element usually overlooked in the learning process of the regressor is the training paradigm. We propose a closed-loop procedure, where the human learns how to improve the quality of the generated EMG signals, helping also to obtain a better controller. We apply it to 10 healthy and 3 limb-deficient subjects. Results show that the combination of the multidimensional targets and the open-loop training protocol significantly improve the performance, increasing the average completion rate from 53% to 65% for the most complicated case of simultaneously controlling the three degrees of freedom.

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“…Compared with the continuous force estimation realized by support vector regression (SVR) and multi-modal feature combination proposed by Mao et al [15], the proposed grasping force estimation method can improve the performance of the CC value by 1.93% and required less training set. On the other hand, compared with a 5-day repeatability experiment showed a 4.1% decrease of classification accuracy per day [35], this method has no significant decline during 30 days with the largest variation of 2.69% under same task.…”

Section: B Huxley-type Musculoskeletal-model-based Grasping Force Est...mentioning

confidence: 99%

“…Fang et al [14] proposed an attribute-driven granular model (AGrM) under a machine-learning scheme to obtain more force level in different pinch-types without continuous force estimation. To realize continuous force estimation and further improve the accuracy, support vector regression (SVR) and multi-modal feature combination is implemented by Mao et al [15] to estimates continuous grip force and further improve the cc value of continuous grip force estimation up to 95.32±1.35%. However, these methods lack an explanation of the muscle's biological mechanism and require a large amount of high-quality training data which result in a long training time and the potential overfitting.…”

Section: Introductionmentioning

confidence: 99%

Continuous Grasping Force Estimation With Surface EMG Based on Huxley-Type Musculoskeletal Model

Deng

Zhang

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Continuous grasping force estimation based on electromyography (EMG) signals, is very useful in practical applications including prosthetic control and human force observation. However, implementing the practical grasping force estimation usually considers a trade-off between the computational precision and resources. Specifically, the estimation based on the Huxley-type muscle model reaches detailed approximation of physiological process at a cost of larger computational resources for solving nonlinear partial differential equations while the counterpart with a traditional Hill-type muscle model. In this article, we achieve the grasping force estimation based on a reduced Huxley-type musculoskeletal model with high accuracy yet low time delay. Leveraging on a balanced truncation method, we further reduce the dimensionality of the spectral method solution in the Huxley-type musculoskeletal model for the model simplification. In addition, we introduce the Kalman filter method to process the EMG signals obtained by an armband, yielding better real-time performances and accuracy compared to the signal treatment using the traditional EMG filter method. Moreover, we also implement an effective identification of the model parameters using a particle swarm method. Finally, we trained the model on the first day and made grasping force estimation experiments involved with three participants over the course of a month. We envision that this effective and practical method would further improve the practical applications in the field of grasping force estimation.

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Simultaneous estimation of grip force and wrist angles by surface electromyography and acceleration signals

Cited by 15 publications

References 37 publications

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches

Simultaneous Three-Degrees-of-Freedom Prosthetic Control Based on Linear Regression and Closed-Loop Training Protocol

Continuous Grasping Force Estimation With Surface EMG Based on Huxley-Type Musculoskeletal Model

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