Separation of Fatigue Content in sEMG Signals Using High Definition Electrodes

Almulla, Mohammed; Sepúlveda, Francisco

doi:10.1145/3209914.3209947

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…Although decoding wearers' electroencephalogram (EEG) information can help identify the motion intention of the lower limbs of humans, some problems exist, such as difficulty in information extraction, frequent interferences, and low accuracy of multijoint action recognition. As a kind of neural signal, EMG has the property of being generated ahead of limb movement [9,10]; therefore, by measuring the EMG of human bodies, the degree of muscle fatigue [11] and the functional state of humans can be measured [12].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Human Motion Intentions Based on Bayesian-Optimized XGBOOST Algorithm

Gao

et al. 2022

Journal of Sensors

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In order to improve the recognition rate for lower extremity motion patterns, this study designs a recognition method for such patterns, which integrates electromyography (EMG) and inertial measurement unit (IMU) signals in three posture modes, including walking on the ground, squatting, and extending seated legs, to address the difficulty with obtaining high signal-to-noise ratio EMG and IMU signals synchronously. Besides, this study proposes a synchronous analysis method for EMG and IMU dual-mode information to correct antipower frequency interference accelerometer signals. The collected signals are preprocessed to extract eigenvalues. And by using the kernel principal component analysis (KPCA), the information on these eigenvalues is fused. Finally, according to the characteristics of the data, a Bayesian-optimized XGBOOST algorithm is designed. Lower-limb movement patterns are classified with the feature vector put into the optimization algorithm. Multiperson experimental results show that the average recognition accuracy for different poses can reach 94.42%, the average F 1 value 95.33%, and the average return value 95.68%, proving that the model proposed can be used to identify human motion intentions and its generalization ability can detect individual differences in human bodies.

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Section: Introductionmentioning

confidence: 99%

Recognition of Human Motion Intentions Based on Bayesian-Optimized XGBOOST Algorithm

Gao

et al. 2022

Journal of Sensors

View full text Add to dashboard Cite

show abstract

Separation of Fatigue Content in sEMG Signals Using High Definition Electrodes

Cited by 1 publication

References 23 publications

Recognition of Human Motion Intentions Based on Bayesian-Optimized XGBOOST Algorithm

Recognition of Human Motion Intentions Based on Bayesian-Optimized XGBOOST Algorithm

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