Online Adaptive Prediction of Human Motion Intention Based on sEMG

Accurate and reliable motion intention perception and prediction are keys to the exoskeleton control system. In this paper, a motion intention prediction algorithm based on sEMG signal is proposed to predict joint angle and heel strike time in advance. To ensure the accuracy and reliability of the p...

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Vydané v:Sensors (Basel, Switzerland) Ročník 21; číslo 8; s. 2882
Hlavní autori: Ding, Zhen, Yang, Chifu, Wang, Zhipeng, Yin, Xunfeng, Jiang, Feng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 20.04.2021
MDPI
Predmet:
Accuracy
Adaptation
Algorithms
Controllers
convolution autoencoder
Electrodes
Electromyography
Exoskeleton Device
Fitness equipment
Human mechanics
Humans
Intention
Motion
motion intention prediction
Muscle fatigue
online adaptation
Reproducibility of Results
sEMG
Sensors
Signal processing
Walking
wearable sensors
motion intention prediction
online adaptation
convolution autoencoder
sEMG
wearable sensors
ISSN:1424-8220, 1424-8220
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Shrnutí:Accurate and reliable motion intention perception and prediction are keys to the exoskeleton control system. In this paper, a motion intention prediction algorithm based on sEMG signal is proposed to predict joint angle and heel strike time in advance. To ensure the accuracy and reliability of the prediction algorithm, the proposed method designs the sEMG feature extraction network and the online adaptation network. The feature extraction utilizes the convolution autoencoder network combined with muscle synergy characteristics to get the high-compression sEMG feature to aid motion prediction. The adaptation network ensures the proposed prediction method can still maintain a certain prediction accuracy even the sEMG signals distribution changes by adjusting some parameters of the feature extraction network and the prediction network online. Ten subjects were recruited to collect surface EMG data from nine muscles on the treadmill. The proposed prediction algorithm can predict the knee angle 101.25 ms in advance with 2.36 degrees accuracy. The proposed prediction algorithm also can predict the occurrence time of initial contact 236±9 ms in advance. Meanwhile, the proposed feature extraction method can achieve 90.71±3.42% accuracy of sEMG reconstruction and can guarantee 73.70±5.01% accuracy even when the distribution of sEMG is changed without any adjustment. The online adaptation network enhances the accuracy of sEMG reconstruction of CAE to 87.65±3.83% and decreases the angle prediction error from 4.03∘ to 2.36∘. The proposed method achieves effective motion prediction in advance and alleviates the influence caused by the non-stationary of sEMG.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s21082882