sEMG Signal-Based Lower Limb Human Motion Detection Using a Top and Slope Feature Extraction Algorithm

Ryu, Jaehwan; Lee, Byeong-Hyeon; Kim, Deok‐Hwan

doi:10.1109/lsp.2016.2636320

Cited by 56 publications

(13 citation statements)

References 12 publications

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“…Ryu et al conducted research on feature extraction of human lower limbs. Their experimental results show that the average detection accuracy of this method in gait subphase detection, motion pattern recognition, and pattern change detection is better than traditional detection results [ 6 ]. Ye et al proposed a human motion analysis algorithm based on skeleton extraction and a dynamic time warping algorithm based on RGBD camera.…”

Section: Related Workmentioning

confidence: 99%

Design of Children’s Motor Skills Training Management System Based on Human Movement Recognition Algorithm of Depth Information

Cheng

2022

Computational Intelligence and Neuroscience

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With the deepening of the concept of all-round development, the training of children’s motor skills is becoming more and more popular. Children’s motor skills training management system is based on the product of children’s motor skills training combined with modern technology. This article aims to design a management system for children’s motor skills training based on human motion recognition methods. This article proposes a human motion recognition algorithm based on depth information. The traditional human motion recognition algorithm is more biased towards children’s motor skills. For the design of the system, this article also pays great attention to children’s experience. After analyzing the performance of the system in this study, it is found that the system is not ideal for multitarget human motion recognition. Therefore, this article optimizes the recognition. After the optimization, the recognition rate of the head, upper body, and lower body of the multiobjective human motion recognition is more than 80%.

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Section: Related Workmentioning

confidence: 99%

Design of Children’s Motor Skills Training Management System Based on Human Movement Recognition Algorithm of Depth Information

Cheng

2022

Computational Intelligence and Neuroscience

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“…For example, some researchers used RMS [17,18], mean absolute value (MAV) [19], autoregressive (AR) [20], variance, and Willison amplitude method to extract surface muscle electrical signal features [21,22]. Some researchers used peak frequency and median frequency analysis [23,24] to convert sEMG signals to the frequency domain and extracted sEMG features.…”

Section: Introductionmentioning

confidence: 99%

Lower Limb Motion Recognition Method Based on Improved Wavelet Packet Transform and Unscented Kalman Neural Network

Shi

Qin

Zhu

et al. 2020

Mathematical Problems in Engineering

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Exoskeleton robot is a typical application to assist the motion of lower limbs. To make the lower extremity exoskeleton more flexible, it is necessary to identify various motion intentions of the lower limbs of the human body. Although more sEMG sensors can be used to identify more lower limb motion intention, with the increase in the number of sensors, more and more data need to be processed. In the process of human motion, the collected sEMG signal is easy to be interfered with noise. To improve the practicality of the lower extremity exoskeleton robot, this paper proposed a wavelet packet transform- (WPT-) based sliding window difference average filtering feature extract algorithm and the unscented Kalman neural network (UKFNN) recognition algorithm. We established an sEMG energy feature model, using a sliding window difference average filtering method to suppress noise interference and extracted stable feature values and using UKF filtering to optimize the neural network weights to improve the adaptability and accuracy of the recognition model. In this paper, we collected the sEMG signals of three muscles to identify six lower limb motion intentions. The average accuracy of 94.83% is proposed in this paper. Experiments show that the algorithm improves the accuracy and anti-interference of motion intention recognition of lower limb sEMG signals. The algorithm is superior to the backpropagation neural network (BPNN) recognition algorithm in the lower limb motion intention recognition and proves the effectiveness, novelty, and reliability of the method in this paper.

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“…Human motion intention recognition is an important part of the human-computer interaction in exercise equipment used in rehabilitation. The goal of human motion intention recognition is to decode the motion intention of the subject's central nervous system accurately and in a timely manner; this includes locomotion mode recognition, and mode change detection [1]- [3], gait sub-phase detection [3], [4], and so on. The motion intention can be used by the controller of the rehabilitation equipment to select the control strategy used during rehabilitation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Knee Joint Moment by Surface Electromyography of the Antagonistic and Agonistic Muscle Pairs

Zhang

Zeng

et al. 2019

IEEE Access

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In lower-limb rehabilitation equipment, the prediction of the knee joint moment using surface electromyography signals is an important method of motion intention recognition. To improve the viability of control by human-computer interactions and to reduce the complexity of the knee joint moment prediction model, this paper presents a prediction model for knee joint moment based on artificial neural networks, in which the knee joint angle, the knee joint angular velocity, and a pair of surface electromyography signals from the antagonistic and agonistic muscles of the knee joint are selected as inputs. Two public databases that include the walking data of hemiplegic patients and healthy people are used to test the effect of muscle pair selection on knee joint moment prediction under non-isometric contraction. The dependence of the model on speed and the individual is also tested. The correlation coefficient and the mean absolute error are used as performance indicators. The results demonstrate that the proposed model can predict the knee joint moment well. Across the difference of speeds and subjects, the choice of muscle pair has no significant effect on the prediction of the knee joint moment. Compared with previous research, the proposed model simplifies the measurement parameters and the signal processing process, reducing the number of sensors used in practical applications, which increases the safety and the fluency of the lower-limb movement.

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sEMG Signal-Based Lower Limb Human Motion Detection Using a Top and Slope Feature Extraction Algorithm

Cited by 56 publications

References 12 publications

Design of Children’s Motor Skills Training Management System Based on Human Movement Recognition Algorithm of Depth Information

Design of Children’s Motor Skills Training Management System Based on Human Movement Recognition Algorithm of Depth Information

Lower Limb Motion Recognition Method Based on Improved Wavelet Packet Transform and Unscented Kalman Neural Network

Prediction of Knee Joint Moment by Surface Electromyography of the Antagonistic and Agonistic Muscle Pairs

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