Joint Torque Estimation Model of sEMG Signal for Arm Rehabilitation Device Using Artificial Neural Network Techniques

Jali, Mohd Hafiz; Izzuddin, Tarmizi Ahmad; Bohari, Zul Hasrizal; Sarkawi, Hafez; Sulaima, Mohamad Fani; Baharom, Mohamad Faizal; Bukhari, W. M.

doi:10.1007/978-3-319-07674-4_63

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…The online execution time cost of forward evaluation of ANN is negligible. As suggested in a previous study by Sartori et al [45] the mean delay of a real-time EMG-driven NMS model is roughly 35 ms. Therefore, hybrid-ANN models are still applicable for real-time applications.…”

Section: Limitationsupporting

confidence: 53%

See 1 more Smart Citation

Ankle Joint Torque Prediction Using an NMS Solver Informed-ANN Model and Transfer Learning

Zhang

Zhu

Gutierrez-Farewik

et al. 2022

IEEE J. Biomed. Health Inform.

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In this work, we predicted ankle joint torque by combining a neuromusculoskeletal (NMS) solver-informed artificial neural network (hybrid-ANN) model with transfer learning based on joint angle and muscle electromyography signals. The hybrid-ANN is an ANN augmented with two kinds of features:(1) experimental measurements -muscle signals and joint angles, and (2) informative physical features extracted from the underlying NMS solver, such as individual muscle force and joint torque. The hybrid-ANN model accuracy in torque prediction was studied in both intra-and inter-subject tests, and compared to the baseline models (NMS and standard-ANN). For each prediction model, seven different cases were studied using data from gait at different speeds and from isokinetic ankle dorsi/plantarflexion motion. Additionally, we integrated a transfer learning method in inter-subject models to improve joint torque prediction accuracy by transferring the learned knowledge from previous participants to a new participant, which could be useful when training data is limited. Our results indicated that better accuracy could be obtained by integrating informative NMS features into a standard ANN model, especially in inter-subject cases; overall, the hybrid-ANN model predicted joint torque with higher accuracy than the baseline models, most notably in inter-subject prediction after adopting the transfer learning technique. We demonstrated the potential of combining physics-based NMS and standard-ANN models with a transfer learning technique in different prediction scenarios. This procedure holds great promise in applications such as assistance-as-needed exoskeleton control strategy design by incorporating the physiological joint torque of the users.

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Section: Limitationsupporting

confidence: 53%

“…It is important to note that we chose the classical type of ANN since it has frequently been applied in joint torque prediction thanks to its functionality and approximation accuracy [15], [44], [45]. Other ANNs that have different structures, such as long short-term memory (LSTM) networks, may have different results.…”

Section: Limitationmentioning

confidence: 99%

Ankle Joint Torque Prediction Using an NMS Solver Informed-ANN Model and Transfer Learning

Zhang

Zhu

Gutierrez-Farewik

et al. 2022

IEEE J. Biomed. Health Inform.

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show abstract

“…Figure 6 plots the EEG-sEMG coherence waveforms for three factors in various bands of the C3 and biceps channels in vertical motion. It can be observed that the frequency bands in which the coefficient values increase significantly are concentrated at [16][17][18][19][20][21][22][23][24][25] Hz. It has coherence in the local frequency band above 25 Hz.…”

Section: Resultsmentioning

confidence: 99%

“…Because our research focuses on the effects of several psychological conditions, only the calculation results of the BPNN are reported in this paper. The artificial neural network is the most popular alternative method [20]. A BPNN is a multilayer feedforward network trained by error inverse propagation algorithm.…”

Section: Regression Methodsmentioning

confidence: 99%

Effects of Proprioception and Visual Focus of Attention on Surface Electromyography Proportional Control

Wang

et al. 2019

Applied Sciences

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This paper aimed to study the influence of proprioception and visual focus of attention on predicting elbow angles. The experiments were conducted in two directions with three psychological factors in each direction. Then, the method of pattern recognition was used to predict the elbow angles. Finally, the accuracy of the predicted error was verified by using the root mean square error, and the coherence coefficient of the myocardium was used to analyze the effect of various factors on the recognition accuracy. The results showed that a change in the subject’s external focus of attention and proprioception improved the recognition accuracy of the elbow. The methods and results of the paper can be the basis to optimize surface electromyography (sEMG) control. Finding the relationship between psychological factors and control could enable improvements in the performance of human–computer interactions.

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“…Artificial Neural Networks (ANNs) is a computational technique aiming to replicate human brain functioning. It is referred from the inventor of the first neurocomputer,says a neural network act as a computing framework made up of a number of basic, highly interconnected handling components which process data by their dynamic state reaction to external input [12], [16], [18].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Intelligent PD Classification via SOM with Optimized Correlation

2019

IJITEE

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The Malaysian power industry was a regulated, organized electricity supply wise inherited from British colonization and showed to be robust and reliable throughout the millennium. The electricity sector is always committed to ensuring that supply reliability is preserved on a continual basis without any major disturbance. The most prevalent downtime causes are closely linked to high voltage (HV) equipment malfunctions or defects, especially transformers, switchgears and cables. The consequences of these occurrences are catastrophic and lead to the loss of millions of dollars in terms of the refurbishment or replacement of HV infrastructure. Power utility company has mostly attempted to stop this event from happening through HV equipment monitoring to classify the irregularity before it is more severe. Partial discharge (PD) event is among the phenomena that is being measured and evaluated. PD is commonly found in solid, gaseous or fluid form correlated with void. In this research, an unsupervised approach to the neural network is recommended for PD classification. This journal introduces the PD classification use an analysis based on the combo of self-organizing maps (SOMs) and correlation analysis. This project established that PD data was successfully correlated and clustered using MATLAB software's SOM Toolbox and correlation tool to identify the type of PD in a HV apparatus.

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Joint Torque Estimation Model of sEMG Signal for Arm Rehabilitation Device Using Artificial Neural Network Techniques

Cited by 8 publications

References 14 publications

Ankle Joint Torque Prediction Using an NMS Solver Informed-ANN Model and Transfer Learning

Ankle Joint Torque Prediction Using an NMS Solver Informed-ANN Model and Transfer Learning

Effects of Proprioception and Visual Focus of Attention on Surface Electromyography Proportional Control

Intelligent PD Classification via SOM with Optimized Correlation

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