Hand Motion Recognition Based on GA Optimized SVM Using sEMG Signals

Yang, Shuo; Chai, Yuchen; Ai, Jikun; Sun, Sheji; Liu, Chong

doi:10.1109/iscid.2018.10134

Cited by 15 publications

(4 citation statements)

References 8 publications

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“…Furthermore, the ‘maximum margin’ approach of SVMs ensures excellent generalization to unseen data, thereby reducing the risk of overfitting, especially when compared to other algorithms that might fit too closely to the noise in the training data. As an example, an SVM has been successfully applied to hand-gesture recognition using surface EMG (sEMG) signals, with researchers achieving high accuracy rates of 99.37% on the training set and 90.33% on the test set [ 90 ], and 89.0% in another study [ 91 ]. However, while SVMs excel at maximizing the margin of separation, they can sometimes be sensitive to the choice of kernel and hyperparameters.…”

Section: Machine-learning Algorithms For Gesture Recognitionmentioning

confidence: 99%

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

Pyun,

Kwon,

Yoo

et al. 2023

National Science Review

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Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered precise recognition and practical applications of this technology. Recent advancements in artificial intelligence technology have made significant strides in extracting features from massive and intricate datasets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques to classify simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training datasets. Thereby, machine-learning techniques have endowed the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human-machine interface. In this review, we provide a comprehensive summary covering materials, structures, and machine-learning algorithms for hand gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

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Section: Machine-learning Algorithms For Gesture Recognitionmentioning

confidence: 99%

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

Pyun,

Kwon,

Yoo

et al. 2023

National Science Review

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“…The base station transmits the sEMG signals to PC with USB, and the PC is equipped with EMGworks Acquisition. According to the literature [34], the main sEMG features are distributed over 10-500 Hz. According to the body's physiological structure and the distribution of leg muscles [35], we selected CH1-right leg rectus femoris, CH2-right biceps femoris, CH3-right tibial anterior muscle, CH4-right gastrocnemius, CH5-left leg rectus femoris, CH6-left biceps femoris, CH7-left tibialis anterior muscle, and CH8-left gastrocnemius as the signal acquisition sources, as shown in Figure 3.…”

Section: Experiments Proceduresmentioning

confidence: 99%

Action Recognition of Lower Limbs Based on Surface Electromyography Weighted Feature Method

Wang

Cao

Liu

2021

Sensors

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To improve the recognition rate of lower limb actions based on surface electromyography (sEMG), an effective weighted feature method is proposed, and an improved genetic algorithm support vector machine (IGA-SVM) is designed in this paper. First, for the problem of high feature redundancy and low discrimination in the surface electromyography feature extraction process, the weighted feature method is proposed based on the correlation between muscles and actions. Second, to solve the problem of the genetic algorithm selection operator easily falling into a local optimum solution, the improved genetic algorithm-support vector machine is designed by championship with sorting method. Finally, the proposed method is used to recognize six types of lower limb actions designed, and the average recognition rate reaches 94.75%. Experimental results indicate that the proposed method has definite potentiality in lower limb action recognition.

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“…A 91.1% classification accuracy was observed for this method. Yang et al [37] classified eight hand motions including palm extension, palm turn downwards, palm turn upwards, palm enstrophe, palm ectropion, fist turn downwards, fist turn upwards, and clenching by using genetic algorithm optimized SVM. Power spectral density was used for feature extraction.…”

Section: Machine Learning Based Discrete-motion Classificationmentioning

confidence: 99%

sEMG Based Human Motion Intention Recognition

Zhang

Liu

Han

et al. 2019

Journal of Robotics

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Human motion intention recognition is a key to achieve perfect human-machine coordination and wearing comfort of wearable robots. Surface electromyography (sEMG), as a bioelectrical signal, generates prior to the corresponding motion and reflects the human motion intention directly. Thus, a better human-machine interaction can be achieved by using sEMG based motion intention recognition. In this paper, we review and discuss the state of the art of the sEMG based motion intention recognition that is mainly used in detail. According to the method adopted, motion intention recognition is divided into two groups: sEMG-driven musculoskeletal (MS) model based motion intention recognition and machine learning (ML) model based motion intention recognition. The specific models and recognition effects of each study are analyzed and systematically compared. Finally, a discussion of the existing problems in the current studies, major advances, and future challenges is presented.

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Hand Motion Recognition Based on GA Optimized SVM Using sEMG Signals

Cited by 15 publications

References 8 publications

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

Action Recognition of Lower Limbs Based on Surface Electromyography Weighted Feature Method

sEMG Based Human Motion Intention Recognition

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