Design and Voluntary Control of Variable Stiffness Exoskeleton Based on sEMG Driven Model

Zhu, Yanghui; Wu, Qingcong; Chen, Bai; Zhao, Ziyue

doi:10.1109/lra.2022.3160668

Cited by 34 publications

(4 citation statements)

References 32 publications

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“…The estimation performance of the trained FG-CNN was evaluated by using two indicators: normal root-mean-squared error (NRMSE) and correlation coefficients (CC), respectively (Kwon and Kim, 2011 ; Qing et al, 2022 ). The NRMSE is used to reflect the deviation between the measured and estimated knee joint angles, in percentage (%) (Zhu et al, 2022 ). The CC value can reflect the strength of the correlation between the measured and estimated knee joint angles, which is close to 1, which indicates meaning a good match between the measurement and the estimation.…”

Section: Methodsmentioning

confidence: 99%

Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network

Zhang

Lu²,

Huo³

et al. 2022

Front. Neurorobot.

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Estimating human motion intention, such as intent joint torque and movement, plays a crucial role in assistive robotics for ensuring efficient and safe human-robot interaction. For coupled human-robot systems, surface electromyography (sEMG) signal has been proven as an effective means for estimating human's intended movements. Usually, joint movement estimation uses sEMG signals measured from multiple muscles and needs many sEMG sensors placed on the human body, which may cause discomfort or result in mechanical/signal interference from wearable robots/environment during long-term routine use. Although the muscle synergy principle implies that it is possible to estimate human motion using sEMG signals from even one signal muscle, few studies investigated the feasibility of continuous motion estimation based on single-channel sEMG. In this study, a feature-guided convolutional neural network (FG-CNN) has been proposed to estimate human knee joint movement using single-channel sEMG. In the proposed FG-CNN, several handcrafted features have been fused into a CNN model to guide CNN feature extraction, and both handcrafted and CNN-extracted features were applied to a regression model, i.e., random forest regression, to estimate knee joint movements. Experiments with 8 healthy subjects were carried out, and sEMG signals measured from 6 muscles, i.e., vastus lateralis, vastus medialis, biceps femoris, semitendinosus, lateral or medial gastrocnemius (LG or MG), were separately evaluated for knee joint estimation using the proposed method. The experimental results demonstrated that the proposed FG-CNN method with single-channel sEMG signals from LG or MG can effectively estimate human knee joint movements. The average correlation coefficient between the measured and the estimated knee joint movements is 0.858 ± 0.085 for LG and 0.856 ± 0.057 for MG. Meanwhile, comparative studies showed that the combined handcrafted-CNN features outperform either the handcrafted features or the CNN features; the performance of the proposed signal-channel sEMG-based FG-CNN method is comparable to those of the traditional multi-channel sEMG-based methods. The outcomes of this study enable the possibility of developing a single-channel sEMG-based human-robot interface for knee joint movement estimation, which can facilitate the routine use of assistive robots.

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Section: Methodsmentioning

confidence: 99%

Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network

Zhang

Lu²,

Huo³

et al. 2022

Front. Neurorobot.

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“…Surface electromyography (sEMG) have been widely implemented in human-machine interface to allow users to communicate with assistive devices, i.e. exoskeletons, prostheses, and orthoses [1][2][3][4]. This is especially the case for myocontrolled hand prostheses or exoskeletons due to the indispensability of human hand in activities of daily living [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Concurrent and continuous estimation of multi-finger forces by synergy mapping and reconstruction: a pilot study

Teng,

Xu,

Zhang

et al. 2023

J. Neural Eng.

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Objective. The absence of intuitive control in present myoelectric interfaces makes it a challenge for users to communicate with assistive devices efficiently in real-world conditions. This study aims to tackle this difficulty by incorporating neurophysiological entities, namely muscle and force synergies, onto multi-finger force estimation to allow intuitive myoelectric control. Approach. Eleven healthy subjects performed six isometric grasping tasks at three muscle contraction levels. The exerted fingertip forces were collected concurrently with the surface electromyographic (sEMG) signals from six extrinsic and intrinsic muscles of hand. Muscle synergies were then extracted from recorded sEMG signals, while force synergies were identified from measured force data. Afterwards, a linear regressor was trained to associate the two types of synergies. This would allow us to predict multi-finger forces simply by multiplying the activation signals derived from muscle synergies with the weighting matrix of initially identified force synergies. To mitigate the false activation of unintended fingers, the force predictions were finally corrected by a finger state recognition procedure. Main results. We found that five muscle synergies and four force synergies are able to make a tradeoff between the computation load and the prediction accuracy for the proposed model; When trained and tested on all six grasping tasks, our method (SYN-II) achieved better performance (R 2 = 0.80 ± 0.04, NRMSE = 0.19 ± 0.01) than conventional sEMG amplitude-based method; Interestingly, SYN-II performed better than all other methods when tested on two unknown tasks outside the four training tasks (R 2 = 0.74 ± 0.03, NRMSE = 0.22 ± 0.02), which indicated better generalization ability. Significance. This study shows the first attempt to link between muscle and force synergies to allow concurrent and continuous estimation of multi-finger forces from sEMG. The proposed approach may lay the foundation for high-performance myoelectric interfaces that allow users to control robotic hands in a more natural and intuitive manner.

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“…Recently, with the rapid development of sensor technology, the human-robot cooperative control based on multi-sensor signals fusion technology is proposed to obtain perfect gait tracking, including plantar pressure sensor, angle position encoder, inertial measurement unit (IMU), and human physiological signals sensors, such as surface electromyogram (sEMG) [25,26], electroencephalogram (EEG), electrooculogram (EOG) [27,28], and brain-machine [29]. Obviously, there are too many signals and too much data in the above continuous system caused by periodic sampling called time-triggered control.…”

Section: Introductionmentioning

confidence: 99%

“…That means when k = n + 1, inequality (25) also holds. Therefore, synthesizing (a), (b), and (c) by the method of mathematical induction, when t ∈ [t k−1 , t k ), k ∈ Z + , inequality (25) always holds.…”

mentioning

confidence: 99%

Event-Triggered Sliding Mode Impulsive Control for Lower Limb Rehabilitation Exoskeleton Robot Gait Tracking

et al. 2023

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Lower limb rehabilitation exoskeleton robots (LLRERs) play an important role in lower limb rehabilitation training and assistance walking for patients with lower limb movement disorders. In order to reduce and eliminate adverse effects on the accuracy of human motion gait tracking during walking with an LLRER, which is caused by the gravity and friction, the periodic ground shock force, and the human–exoskeleton interaction force, this paper proposes a feedforward–feedback hybrid control strategy of sliding mode impulsive control with gravity and friction compensation, based on the event-triggered mechanism of Lyapunov function. Firstly, to realize high-precision gait tracking with bounded error, some constraints on controller parameters are deduced by analyzing the Lyapunov-based stability. Secondly, the Zeno behavior of impulsive event triggers is excluded by the analysis of three different cases of the triggering time sequence. Finally, the effectiveness of the proposed hybrid controller is verified by the numerical simulation of the LLRER human–exoskeleton integrated system based on a three-link simplified model. It shows that an event-triggered sliding mode impulsive control strategy with gravity and friction compensation can achieve complete gait tracking with bounded error and has excellent dynamic performance under the constraints.

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Design and Voluntary Control of Variable Stiffness Exoskeleton Based on sEMG Driven Model

Cited by 34 publications

References 32 publications

Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network

Estimation of knee joint movement using single-channel sEMG signals with a feature-guided convolutional neural network

Concurrent and continuous estimation of multi-finger forces by synergy mapping and reconstruction: a pilot study

Event-Triggered Sliding Mode Impulsive Control for Lower Limb Rehabilitation Exoskeleton Robot Gait Tracking

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