Pattern recognition of EMG signals for low level grip force classification

Khan, Salman Mohd; Khan, Abid Ali; Farooq, Omar

doi:10.1088/2057-1976/ac2354

Cited by 14 publications

(9 citation statements)

References 47 publications

(73 reference statements)

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“…The use of kinetic, kinematics and neural signals (EMG) is not uncommon for movement classification [13], [14], [15]. Based on the application of our study, the duration and strength of IBMs, kinetics, and kinematics are better choices for signal classification.…”

Section: Discussionmentioning

confidence: 98%

Involuntary breathing movement pattern recognition and classification via force based sensors

Singh

Fleury

Gupta

et al. 2022

Preprint

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The study presents a novel scheme that recognizes and classifies different subphases within the involuntary breathing movement (IBM) phase during breath-holding (BH). We collected force data from eight recreational divers until the conventional breakpoint (CB). They were in a supine position on force plates. We segmented their data into the no-movement (NM) phase aka easy phase and IBM phase (comprising several events or sub-phases of IBM). The acceleration and jerk were estimated from the data to quantify the IBMs, and phase portraits were developed to select and extract specific features. The K means clustering was performed on these features to recognize different sub-phases within the IBM phase. We found five-six optimal clusters separating different sub-phases within the IBM phase. These clusters separating different sub-phases have physiological relevance to internal struggle and were labeled as classes for classification using support vector machine (SVM), naive bayes (NB), decision tree (DT), and K-nearest neighbor (K-NN). In comparison with no feature selection and extraction, we found that our phase portrait method of feature selection and extraction had a low computational cost and high robustness of 96–99% accuracy.

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Section: Discussionmentioning

confidence: 98%

Involuntary breathing movement pattern recognition and classification via force based sensors

Singh

Fleury

Gupta

et al. 2022

Preprint

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“… Cracchiolo et al (2021) designed three different levels of grip force to classify different forces and achieved a recognition rate of 72.2%. Khan et al (2021) also classified different levels of pinch grasping forces, with an accuracy ranging from 91.7 to 94.5%. However, their work was to investigate the possibility of identifying different levels of force in one motion.…”

Section: Discussionmentioning

confidence: 99%

“…Cracchiolo et al (2021) designed three different levels of grip force to classify different forces and achieved a recognition rate of 72.2% Khan et al (2021). also classified different levels…”

mentioning

confidence: 99%

Natural grasping movement recognition and force estimation using electromyography

Zhang²,

Yang³

et al. 2022

Front. Neurosci.

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Electromyography (EMG) generated by human hand movements is usually used to decode different action types with high accuracy. However, the classifications of the gestures rarely consider the impact of force, and the estimation of the grasp force when performing natural grasping movements is so far overlooked. Decoding natural grasping movements and estimating the force generated by the associated movements can help patients to improve the accuracy of prosthesis control. This study mainly focused on two aspects: the classification of four natural grasping movements and the force estimation of these actions. For this purpose, we designed an experimental platform where subjects could perform four common natural grasping movements in daily life, including pinch, palmar, twist, and plug grasp, to complete target profiles. On the one hand, the results showed that, for natural grasping movements with different levels of force (three levels at 20, 50, and 80%), the average accuracy could reach from 91.43 to 97.33% under five classification schemes. On the other hand, the feasibility of force estimation for natural grasping movements was demonstrated. Furthermore, in the process of force estimation, we confirmed that the regression performance about plug grasp was the best, and the average R2 could reach 0.9082. Besides, we found that the regression results were affected by the speed of force application. These findings contribute to the natural control of myoelectric prosthesis and the EMG-based rehabilitation training system, improving the user’s experience and acceptance.

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“…In this study, two kernels were used, namely linear SVM-L and radial basis function SVM-RBF. Random Forest (RF) trains all features using a decision tree and then classifies the samples to minimize the variance [66].…”

Section: Classificationmentioning

confidence: 99%

Dual-Layer Ranking Feature Selection Method Based on Statistical Formula for Driver Fatigue Detection of EMG Signals

Naim¹,

Mustafa²,

Sulaiman³

et al. 2022

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Electromyography (EMG) signals are one of the most studied inputs for driver drowsiness detection systems. As the number of EMG features available can be daunting, finding the most significant and minimal subset features is desirable. Hence, a simplified feature selection method is necessary. This work proposed a dual-layer ranking feature selection algorithm based on statistical formula f EMG signals for driver fatigue detection. In the beginning, in the first layer, 21 filter algorithms were calculated to rank 47 sets of EMG features (25 time-domain and 9 frequency-domain) and applied to six classifiers. Then, in the second layer, all the ranks were re-ranked based on the statistical formula (average, median, mode and variance). The classification performance of all rankings was compared along with the number of features. The highest classification accuracy achieved was 95% for 12 features using the Average Statistical Rank (ASR) and LDA classifier. It is conclusive that a combination of features from the time domain and frequency domain can deliver better performance compared to a single domain feature. Concurrently, the statistical rank ASR performed better than the single filter rank by reducing the number of features. The proposed model can be a benchmark for the enhanced feature selection method for EMG driver fatigue signal.

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Pattern recognition of EMG signals for low level grip force classification

Cited by 14 publications

References 47 publications

Involuntary breathing movement pattern recognition and classification via force based sensors

Involuntary breathing movement pattern recognition and classification via force based sensors

Natural grasping movement recognition and force estimation using electromyography

Dual-Layer Ranking Feature Selection Method Based on Statistical Formula for Driver Fatigue Detection of EMG Signals

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