Muscle Synergy Alteration of Human During Walking With Lower Limb Exoskeleton

Li, Zhan; Liu, Huxian; Yin, Ziguang; Chen, Kejia

doi:10.3389/fnins.2018.01050

Cited by 27 publications

(17 citation statements)

References 21 publications

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“…According to Sylos-Labini et al ( 2014 ), the assistive forces provided by an exoskeleton may result in a change of the muscle coordination manners (i.e., muscle synergies). Similar conclusions were also given by the related studies that investigated the effect of robotic gait assistance on the muscle function of the subjects (Moreno et al, 2013 ; Li et al, 2019 ). The altered muscle functions would cause an unknown distribution change of sEMG, therefore cause adverse effects on the sEMG-based motion-decoding methods.…”

Section: Introductionsupporting

confidence: 83%

“…How the exoskeletons affect the muscle functions of the subjects have been investigated for many years (Steele et al, 2017 ). Prior studies have revealed that external forces that were provided by the exoskeletons would alter the activity-level and recruitment patterns of the muscle groups (Sylos-Labini et al, 2014 ; Li et al, 2019 ). The study (Sylos-Labini et al, 2014 ) recorded the sEMG activity of six healthy individuals during overground walking with a lower-limb exoskeleton.…”

Section: Related Workmentioning

confidence: 99%

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A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons

Wei

Ding

et al. 2021

Front. Neurorobot.

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The interaction between human and exoskeletons increasingly relies on the precise decoding of human motion. One main issue of the current motion decoding algorithms is that seldom algorithms provide both discrete motion patterns (e.g., gait phases) and continuous motion parameters (e.g., kinematics). In this paper, we propose a novel algorithm that uses the surface electromyography (sEMG) signals that are generated prior to their corresponding motions to perform both gait phase recognition and lower-limb kinematics prediction. Particularly, we first propose an end-to-end architecture that uses the gait phase and EMG signals as the priori of the kinematics predictor. In so doing, the prediction of kinematics can be enhanced by the ahead-of-motion property of sEMG and quasi-periodicity of gait phases. Second, we propose to select the optimal muscle set and reduce the number of sensors according to the muscle effects in a gait cycle. Finally, we experimentally investigate how the assistance of exoskeletons can affect the motion intent predictor, and we propose a novel paradigm to make the predictor adapt to the change of data distribution caused by the exoskeleton assistance. The experiments on 10 subjects demonstrate the effectiveness of our algorithm and reveal the interaction between assistance and the kinematics predictor. This study would aid the design of exoskeleton-oriented motion-decoding and human–machine interaction methods.

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Section: Introductionsupporting

confidence: 83%

Section: Related Workmentioning

confidence: 99%

A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons

Wei

Ding

et al. 2021

Front. Neurorobot.

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“…These regularities are called “muscle synergies”. The limb movements are accomplished by activating these synergies coordinately [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Muscle Synergy Extraction Method Used for Motor Function Evaluation of Stroke Patients: A Pilot Study

Shi

et al. 2021

Sensors

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In this paper, we present a novel muscle synergy extraction method based on multivariate curve resolution–alternating least squares (MCR-ALS) to overcome the limitation of the nonnegative matrix factorization (NMF) method for extracting non-sparse muscle synergy, and we study its potential application for evaluating motor function of stroke survivors. Nonnegative matrix factorization (NMF) is the most widely used method for muscle synergy extraction. However, NMF is susceptible to components’ sparseness and usually provides inferior reliability, which significantly limits the promotion of muscle synergy. In this study, MCR-ALS was employed to extract muscle synergy from electromyography (EMG) data. Its performance was compared with two other matrix factorization algorithms, NMF and self-modeling mixture analysis (SMMA). Simulated data sets were utilized to explore the influences of the sparseness and noise on the extracted synergies. As a result, the synergies estimated by MCR-ALS were the most similar to true synergies as compared with SMMA and NMF. MCR-ALS was used to analyze the muscle synergy characteristics of upper limb movements performed by healthy (n = 11) and stroke (n = 5) subjects. The repeatability and intra-subject consistency were used to evaluate the performance of MCR-ALS. As a result, MCR-ALS provided much higher repeatability and intra-subject consistency as compared with NMF, which were important for the reliability of the motor function evaluation. The stroke subjects had lower intra-subject consistency and seemingly had more synergies as compared with the healthy subjects. Thus, MCR-ALS is a promising muscle synergy analysis method for motor function evaluation of stroke patients.

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“…Since it is quite difficult to measure multiple joint torques and ground reaction forces simultaneously for the human-exoskeleton system, employing gait data acquired by motion capture devices is more convenient for the control system to process and configure [7]. Due to different influence factors such as subjects' biomechanics and physiological movement diversity, gait kinematic signals can be still characterized as nonlinear and nonstationary signals [8].…”

Section: Introductionmentioning

confidence: 99%

Gait Recognition for Human-Exoskeleton System in Locomotion Based on Ensemble Empirical Mode Decomposition

Qiu

Liu

2021

Mathematical Problems in Engineering

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As exoskeleton robots are more frequently applied to impaired people to regain mobility, detection and recognition of human gait motions is important to prepare suitable control modes for exoskeletons. This paper proposes to explore the potential of the ensemble empirical mode decomposition (EEMD) method to help analyze and recognize gait motions for human subjects who wear the exoskeleton to walk. The intrinsic mode functions (IMFs) extracted from the original gait signals by EEMD are utilized to act as inputs for classification algorithms. Evident correlations are found between some IMFs and original gait kinematic sequences. Experimental results on gait phase recognition performance on 14 able-bodied subjects are shown. The performance of the composing signals extracted from the original signals as IMF 1 ∼ IMF 8 is investigated, which indicates that IMF 8 might be helpful when wearing exoskeleton and IMF 5 might be helpful when walking without exoskeleton on gait recognition. And the similarity of joint synergy between wearing and without wearing exoskeleton is analyzed, and the result shows that the joint synergy might change between with and without wearing exoskeleton. The quantitative results show that based on some IMFs of the same orders, these machine learning algorithms can achieve promising performances.

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Muscle Synergy Alteration of Human During Walking With Lower Limb Exoskeleton

Cited by 27 publications

References 21 publications

A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons

A Novel sEMG-Based Gait Phase-Kinematics-Coupled Predictor and Its Interaction With Exoskeletons

A Novel Muscle Synergy Extraction Method Used for Motor Function Evaluation of Stroke Patients: A Pilot Study

Gait Recognition for Human-Exoskeleton System in Locomotion Based on Ensemble Empirical Mode Decomposition

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