Abstract
The human–robot interface (HRI) based on biological signals can realize the natural interaction between human and robot. It has been widely used in exoskeleton robots recently to help predict the wearer's movement. Surface electromyography (sEMG)-based HRI has mature applications on the exoskeleton. However, the sEMG signals of paraplegic patients' lower limbs are weak, which means that most HRI based on lower limb sEMG signals cannot be applied to the exoskeleton. Few studies have explored the possibility of using upper limb sEMG signals to predict lower limb movement. In addition, most HRIs do not consider the contribution and synergy of sEMG signal channels. This paper proposes a human–exoskeleton interface based on upper limb sEMG signals to predict lower limb movements of paraplegic patients. The interface constructs an channel synergy-based network (MCSNet) to extract the contribution and synergy of different feature channels. An sEMG data acquisition experiment is designed to verify the effectiveness of MCSNet. The experimental results show that our method has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 94.51 and 80.75%, respectively. Furthermore, feature visualization and model ablation analysis show that the features extracted by MCSNet are physiologically interpretable.
Highlights
The development of artificial intelligence technology and wearable sensors has promoted the rise of human–robot interaction
The proposed movement prediction model uses long short-term memory networks (LSTM), depthwise and separable convolutions to extract the spatiotemporal features of multi-channel Surface electromyography (sEMG) signals, and introduces an attention module to extract the synergy of different sEMG feature channels
An sEMG signal acquisition experiment based on upper limb muscles is designed to verify the effectiveness of the method proposed in this paper
Summary
The development of artificial intelligence technology and wearable sensors has promoted the rise of human–robot interaction. Exoskeleton is a typical application scenario of HRI, and some HRI based on physical signals, such as inertial measurement units or pressure signals, have been used in the walking-assistant exoskeleton to realize the movement prediction of patients with hemiplegia/paraplegia (Beil et al, 2018; Ding et al, 2020; Zhu et al, 2020a). With the decoding of biological signals, HRI based on biological signals (such as electroencephalogram and electromyography) have been designed, MCSNet: Channel Synergy-Based Human-Exoskeleton Interface opening up the possibility of realizing more natural and efficient movement predictions between human and exoskeleton (Suplino et al, 2019; Ortiz et al, 2020; Zhuang et al, 2021). It is urgent to propose an HRI with high movement prediction accuracy for paraplegic patients
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