Force Estimation Based on sEMG using Wavelet Analysis and Neural Network

Du, Jiang; Li, Gongfa; Jiang, Guozhang; Chen, Disi; Ju, Zhaojie

doi:10.1109/icist.2019.8836897

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…Although prior studies that employed ANNs achieved relatively high prediction accuracy, there are some limitations with their approach. Some models only predicted force accurately at specific hand postures (Choi et al, 2010; Ma et al, 2020), whereas another only analyzed accuracy among the training dataset (Jiang et al, 2019). In addition, no prior studies that the authors are aware of have explored the accuracy of hand posture or force prediction while varying repetition rate and duty cycle in a novel dataset, both of which are important characteristics of tasks performed in the field.…”

Section: Discussionmentioning

confidence: 99%

“…Despite recent studies that have successfully shown the value of using ANNs to predict grip force in controlled settings (Choi et al, 2010; Jiang et al, 2019; Ma et al, 2020), ANNs have not yet been used to predict hand posture or force during tasks with varying repetition rate or duty cycle (time spent in exertion). Additionally, prior studies have applied ANN prediction models to similar datasets by splitting the data into training and test datasets and reporting prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Hand Posture and Force Estimation Using Surface Electromyography and an Artificial Neural Network

et al. 2021

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Objective The purpose of this study was to develop an approach to predict hand posture (pinch versus grip) and grasp force using forearm surface electromyography (sEMG) and artificial neural networks (ANNs) during tasks that varied repetition rate and duty cycle. Background Prior studies have used electromyography with machine learning models to predict grip force but relatively few studies have assessed whether both hand posture and force can be predicted, particularly at varying levels of duty cycle and repetition rate. Method Fourteen individuals participated in this experiment. sEMG data for five forearm muscles and force output data were collected. Calibration data (25, 50, 75, 100% of maximum voluntary contraction (MVC)) were used to train ANN models to predict hand posture (pinch versus grip) and force magnitude while performing tasks that varied load, repetition rate, and duty cycle. Results Across all participants, overall hand posture prediction accuracy was 79% (0.79 ± .08), whereas overall hand force prediction accuracy was 73% (0.73 ± .09). Accuracy ranged between 0.65 and 0.93 based on varying repetition rate and duty cycle. Conclusion Hand posture and force prediction were possible using sEMG and ANNs, though there were important differences in the accuracy of predictions based on task characteristics including duty cycle and repetition rate. Application The results of this study could be applied to the development of a dosimeter used for distal upper extremity biomechanical exposure measurement, risk assessment, job (re)design, and return to work programs.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hand Posture and Force Estimation Using Surface Electromyography and an Artificial Neural Network

et al. 2021

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“…As reported in the literature review, extracting discriminative features from 1-D ECG signals ignores their frequency domain characteristics and lacks discriminatory features [6][7], [23]. Moreover, performing the classification via 2D images format has reached higher than using the 1D time-domain signal.…”

Section: -D Mapping Generation Using Time-frequency Techniquesmentioning

confidence: 99%

“…Some of the literature methods classified different types of muscle movements based on sEMG signals. Jiang et al [23] presented an sEMG signals analysis method using discrete Wavelet Transform (DWT) for detecting and characterizing signal patterns. They adopted traditional feature extraction techniques using an external feature extraction process.…”

Section: Introductionmentioning

confidence: 99%

Lower Limb Analysis Based on Surface Electromyography (sEMG) Using Different Time-frequency Representation Techniques

Ibraheem¹

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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Using time-frequency representation techniques, projecting 1D sEMG signals onto a 2D image space can help diagnose several muscle activities. The acquired sEMG signal can provide valuable representative information about the muscle activity firing rates during muscle contraction. Different phases of muscle activity can be discernible via the sEMG signals by extracting discriminating features. The behavior of muscle activity was acquired in measurements of five muscles, i.e., RF, BF, VM, ST, and FX. Previous attempts to visualize lower limb analysis to extract sEMG features adopted One-dimensional (1D) sEMG segments. This work proposes a comparative experiment between three time-frequency representation techniques. The three time-frequency representation techniques, scalogram, spectrogram, and persistence spectrum, were used to map muscles' (1D) sEMG signal straightening the knee. The two-dimensional (2D) projected images are then fed into a convolutional neural network (CNN) model for detecting knee abnormality. The experiments are performed via 10-fold cross-validation. The number of kernels is incremented along with model layers. The fully connected layers were adjusted according to the loss value. Besides, tuning the hyper-parameters of the dropout parameters and the ReLU activation function to verify optimal performance. This research shows that the scalogram image representation gives significantly better performance than the spectrogram and persistence spectrum in recognizing knee abnormality. In addition, this study may help in guiding the diagnosis of several human muscle activities via the sEMG signal. A more diverse of muscles can be further investigated and can be useful for future work to enhance the diagnosis accuracy.

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“…Studies have shown that machine learning is a more robust approach to predicting force or classification problems (Subasi et al, 2006;Choi et al, 2010). Recent studies have successfully reported the accuracy of using artificial neural networks (ANN) to predict grip force in controlled settings (Choi et al 2010;Jiang et al, 2019;Ma et al, 2020;Wang et al, 2020). Accuracy computed based on confusion matrices has been the most popular adopted metrics in binary classification tasks.…”

Section: Introductionmentioning

confidence: 99%

Using a Binary Classification Approach to Assess the Accuracy of Hand Posture and Force Estimation with Machine Learning Models

Wang

Zhao

Barr

et al. 2021

Proceedings of the Human Factors and Ergonomics Society Annual

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Recent studies have successfully reported the accuracy of using artificial neural networks to predict grip force in controlled settings. However, only relying on accuracy to evaluate the machine learning models may lead to overoptimistic results, especially on imbalanced datasets. The Matthews correlation coefficient (MCC) showed an advantage in capturing all the data characteristics in the confusion matrix. Therefore, a binary classification approach and the MCC value were introduced to assess the performance of previously proposed machine learning models. Our results show that the overall correlations ranging between 0.48 and 0.59 indicate a strong relationship between predictions and actual scenarios. The binary classification approach and the MCC values could be used for future performance comparison with other machine learning models.

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Force Estimation Based on sEMG using Wavelet Analysis and Neural Network

Cited by 4 publications

References 24 publications

Hand Posture and Force Estimation Using Surface Electromyography and an Artificial Neural Network

Hand Posture and Force Estimation Using Surface Electromyography and an Artificial Neural Network

Lower Limb Analysis Based on Surface Electromyography (sEMG) Using Different Time-frequency Representation Techniques

Using a Binary Classification Approach to Assess the Accuracy of Hand Posture and Force Estimation with Machine Learning Models

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