The recognition of grasping force using LDA

Wang, Nianfeng F.; Lao, Kunyi; Zhang, Xinhao; Lin, Jinfan; Zhang, Xianmin

doi:10.1016/j.bspc.2018.06.011

Cited by 33 publications

(24 citation statements)

References 17 publications

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“…We have recovered the envelope of the signals with a low pass filter using a 6th order Butterworth filter to cut off frequency at 1Hz and finally normalise the data accordingly using Min-Max normalisation in the data preprocessing stage. The Min-Max normalisation is defined as follows: (11) with: d n : the normalised value, d r : the real value, d min : the minimum value, d max : the maximum value. Then, data were sent to the proposed approach, as described above, for force estimation and compared with performance based on the artificial neural network as previously suggested.…”

Section: Discussionmentioning

confidence: 99%

“…Within our scope for controlling upper limb myoelectric prostheses, accurate estimation of the overall force from the EMG drive proportional control-based schemes. Therefore, many black-box models and statistical identification techniques are proposed [ 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. The degree of linearity between the EMG and muscle force was debated for many years.…”

Section: Introductionmentioning

confidence: 99%

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Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals

2022

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This paper develops a novel approach to characterise muscle force from electromyography (EMG) signals, which are the electric activities generated by muscles. Based on the nonlinear Hammerstein–Wiener model, the first part of this study outlines the estimation of different sub-models to mimic diverse force profiles. The second part fixes the appropriate sub-models of a multimodel library and computes the contribution of sub-models to estimate the desired force. Based on a pre-existing dataset, the obtained results show the effectiveness of the proposed approach to estimate muscle force from EMG signals with reasonable accuracy. The coefficient of determination ranges from 0.6568 to 0.9754 using the proposed method compared with a range of 0.5060 to 0.9329 using an artificial neural network (ANN), generating significantly different accuracy (p < 0.03). Results imply that the use of multimodel approach can improve the accuracy in proportional control of prostheses.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals

2022

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“…In recent days, the myoelectric control has been received much attentions from the biomedical researchers. The correlation between amplitude and motion grants the EMG signal to become one of the most powerful sources in controlling the prosthesis [3].…”

Section: Introductionmentioning

confidence: 99%

Classification of Hand Movements based on Discrete Wavelet Transform and Enhanced Feature Extraction

Too¹,

Abdullah²,

Saad³

2019

IJACSA

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Extraction of potential electromyography (EMG) features has become one of the important roles in EMG pattern recognition. In this paper, two EMG features, namely, enhanced wavelength (EWL) and enhanced mean absolute value (EMAV) are proposed. The EWL and EMAV are the modified version of wavelength (WL) and mean absolute value (MAV), which aims to enhance the prediction accuracy for the classification of hand movements. Initially, the proposed features are extracted from the EMG signals via discrete wavelet transform (DWT). The extracted features are then fed into the machine learning algorithm for classification process. Four popular machine learning algorithms include k-nearest neighbor (KNN), linear discriminate analysis (LDA), Naïve Bayes (NB) and support vector machine (SVM) are used in evaluation. To examine the effectiveness of EWL and EMAV, several conventional EMG features are used in performance comparison. In addition, the efficacy of EWL and EMAV when combine with other features are also investigated. Based on the results obtained, the combination of EWL and EMAV with other features can improve the classification performance. Thus, EWL and EMAV can be considered as valuable tools for rehabilitation and clinical applications.

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“…The support vector machine (SVM) [ 19 ], LDA [ 20 ], and Gaussian mixture models (GMM) [ 18 ] are widely applied for classification of robot-acquired signals. The performance of the technologies discussed above greatly depends on the feature selection of signals.…”

Section: Introductionmentioning

confidence: 99%

Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation

Guo

Yang

et al. 2020

Computational Intelligence and Neuroscience

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Deep-learning models can realize the feature extraction and advanced abstraction of raw myoelectric signals without necessitating manual selection. Raw surface myoelectric signals are processed with a deep model in this study to investigate the feasibility of recognizing upper-limb motion intents and real-time control of auxiliary equipment for upper-limb rehabilitation training. Surface myoelectric signals are collected on six motions of eight subjects’ upper limbs. A light-weight convolutional neural network (Lw-CNN) and support vector machine (SVM) model are designed for myoelectric signal pattern recognition. The offline and online performance of the two models are then compared. The average accuracy is (90 ± 5)% for the Lw-CNN and (82.5 ± 3.5)% for the SVM in offline testing of all subjects, which prevails over (84 ± 6)% for the online Lw-CNN and (79 ± 4)% for SVM. The robotic arm control accuracy is (88.5 ± 5.5)%. Significance analysis shows no significant correlation ( p = 0.056) among real-time control, offline testing, and online testing. The Lw-CNN model performs well in the recognition of upper-limb motion intents and can realize real-time control of a commercial robotic arm.

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The recognition of grasping force using LDA

Cited by 33 publications

References 17 publications

Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals

Hammerstein–Wiener Multimodel Approach for Fast and Efficient Muscle Force Estimation from EMG Signals

Classification of Hand Movements based on Discrete Wavelet Transform and Enhanced Feature Extraction

Lw-CNN-Based Myoelectric Signal Recognition and Real-Time Control of Robotic Arm for Upper-Limb Rehabilitation

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