Neuromuscular Activation Based SEMG-Torque Hybrid Modeling and Optimization for Robot Assisted Neurorehabilitation

Wang, Weiqun; Hou, Zeng-Guang; Shi, Weiguo; Liang, Xu; Ren, Shixin; Wang, Jiaxing; Peng, Liang

doi:10.1007/978-3-030-36711-4_50

Cited by 4 publications

(6 citation statements)

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“…Next to this, the reliability of these methods depend on correct electrode placement and are thus sensitive to changes in conditions. Futhermore, the amount of training data is also usually limited relative to the complexity of the models, which makes it difficult to obtain a satisfactory generalization performance ( Wang W. et al, 2019 ). The robustness to EMG electrode placement, differences in EMG signals (quality), activity performance and performance over days is thus questionable and needs futher investigation.…”

Section: Introductionmentioning

confidence: 99%

“…Another benefit of using an NMS model is that it provides insight in the underlying process of biomechanical movement, whereas machine learning does not. Several studies used an NMS model to predict joint torque ( Wang W. et al, 2019 ; Sartori et al, 2018 ; Durandau et al, 2019 , 2018 ; Sartori et al, 2012 , 2016 ; Kapelner et al, 2020 ). Sartori et al (2012) developed a control scheme to control a wrist-hand prosthesis by real-time neuromusculoskeletal modelling.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Day EMG-Based Knee Joint Torque Estimation Using Hybrid Neuromusculoskeletal Modelling and Convolutional Neural Networks

et al. 2022

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Proportional control using surface electromyography (EMG) enables more intuitive control of a transfemoral prosthesis. However, EMG is a noisy signal which can vary over time, giving rise to the question what approach for knee torque estimation is most suitable for multi-day control. In this study we compared three different modelling frameworks to estimate knee torque in non-weight-bearing situations. The first model contained a convolutional neural network (CNN) which mapped EMG to knee torque directly. The second used a neuromusculoskeletal model (NMS) which used EMG, muscle tendon unit lengths and moment arms to compute knee torque. The third model (Hybrid) used a CNN to map EMG to specific muscle activation, which was used together with NMS components to compute knee torque. Multi-day measurements were conducted on ten able-bodied participants who performed non-weight bearing activities. CNN had the best performance in general and on each day (Normalized Root Mean Squared Error (NRMSE) 9.2 ± 4.4%). The Hybrid model (NRMSE 12.4 ± 3.4%) was able to outperform NMS (NRMSE 14.3 ± 4.2%). The NMS model showed no significant difference between measurement days. The CNN model and Hybrid models had significant performance differences between the first day and all other days. CNNs are suited for multi-day torque estimation in terms of error rate, outperforming the other two model types. NMS was the only model type which was robust over all days. This study investigated the behavior of three model types over multiple days, giving insight in the most suited modelling approach for multi-day torque estimation to be used in prosthetic control.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Day EMG-Based Knee Joint Torque Estimation Using Hybrid Neuromusculoskeletal Modelling and Convolutional Neural Networks

et al. 2022

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“…This is one of the reasons that data-driven intent recognition has become more popular in the past few years [15,18], as data-driven algorithms are able to find relations between sensor data and motion intent automatically, with limited human interference. However, these algorithms could suffer from robustness issues for unseen activities [160,161].…”

Section: Data-driven Intent Recognitionmentioning

confidence: 99%

“…Next to this, the reliability of these methods depend on correct electrode placement and are thus sensitive to changes in conditions. Furthermore, the amount of training data is also usually limited relative to the complexity of the models, which makes it difficult to obtain a satisfactory generalization performance [161]. The robustness to EMG electrode placement, differences in EMG signals (quality), activity performance and performance over days is thus questionable and needs further investigation.…”

Section: Introductionmentioning

confidence: 99%

“…Another benefit of using an NMS model is that it provides insight in the underlying process of biomechanical movement, whereas machine learning does not. Several studies used an NMS model to predict joint torque [160][161][162][163][164][165][166]. [164] developed a control scheme to control a wrist-hand prosthesis by real-time neuromusculoskeletal modeling.…”

Section: Introductionmentioning

confidence: 99%

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Up to one's knees in data : data-driven intent recognition using electromyography for the lower limb

Schulte¹

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Approximately two thousand people undergo a major lower-limb amputation in the Netherlands per year. Reasons for amputation vary, but in high income countries vascular diseases are the main cause for amputation [1-5], followed by trauma [1, 6].A lower-limb amputation can drastically change a person's functional abilities, which leads to a lower quality of life [1, 7]. Individuals with an amputation regain some of those functional abilities by learning to use a prosthesis [8]. Most of the prosthetic components are passive and are adequate for most walking scenarios in daily life.However, these devices are not capable of generating power to provide support during standing up and stair walking for instance [8][9][10]. This means that the residual leg Chapter 2

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Prediction of Elbow Torque Using Improved African Vultures Optimization Algorithm in Neuromusculoskeletal Model

Xia,

Liu,

Zhu

et al. 2023

Lecture Notes in Computer Science

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Neuromuscular Activation Based SEMG-Torque Hybrid Modeling and Optimization for Robot Assisted Neurorehabilitation

Cited by 4 publications

References 20 publications

Multi-Day EMG-Based Knee Joint Torque Estimation Using Hybrid Neuromusculoskeletal Modelling and Convolutional Neural Networks

Multi-Day EMG-Based Knee Joint Torque Estimation Using Hybrid Neuromusculoskeletal Modelling and Convolutional Neural Networks

Up to one's knees in data : data-driven intent recognition using electromyography for the lower limb

Prediction of Elbow Torque Using Improved African Vultures Optimization Algorithm in Neuromusculoskeletal Model

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