Improving the Transparency of an Exoskeleton Knee Joint Based on the Understanding of Motor Intent Using Energy Kernel Method of EMG

Chen, Xing; Zeng, Yan; Yin, Yilong

doi:10.1109/tnsre.2016.2582321

Cited by 58 publications

(29 citation statements)

References 33 publications

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“…Supposing that the attachment between human and exoskeleton is rigid, which has been proved to be feasible in human-exoskeleton systems [21], the current and past joint angles of both human and exoskeleton can be thought of as the same. However, there must be errors in the predicted joint angle of humans, which would result in the interactive torque between the human and exoskeleton, as illustrated in Figure 2.…”

Section: Joint Motion Model Under Human-exoskeleton Interactionmentioning

confidence: 99%

“…When the history of the movements reflects the inherent dynamics of human motion [20], joint angle could also be predicted using autoregressive models. To combine the advantages of both and achieve continuous, intuitive and naturalistic understanding of human motion intent, nonlinear autoregressive with exogenous inputs (NARX) would be a better choice and has begun to be applied in the field of joint angle prediction [4,21,22]. Its application combines the non-linear spatio-temporal correlation structure of natural human movements with muscle-driven control signals to exploit the best of both worlds [23].…”

Section: Introductionmentioning

confidence: 99%

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Gaussian Process-Integrated State Space Model for Continuous Joint Angle Prediction from EMG and Interactive Force in a Human-Exoskeleton System

2019

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As one of the most direct indicators of the transparency between a human and an exoskeleton, interactive force has rarely been fused with electromyography (EMG) in the control of human-exoskeleton systems, the performances of which are largely determined by the accuracy of the continuous joint angle prediction. To achieve intuitive and naturalistic human intent learning, a state space model (SSM) for continuous angle prediction of knee joint is developed. When the influence of the interactive force is often ignored in the existing models of human-exoskeleton systems, interactive force is applied as the measurement model output of the proposed SSM, and the EMG signal is used as the state model input signal to indicate muscle activation. The forward dynamics of joint motion and the human-machine interaction mechanism, i.e., the biomechanical interpretations of the interactive force generation mechanism, are derived as the bases for the state model and measurement model based on Hill’s muscle model and semiphenomenological (SP) muscular model, respectively. Gaussian process (GP)-based nonlinear autoregressive with the exogenous inputs (NARX) model and back-propagation neural network (BPNN) are applied to provide better adaptivity for the SSM in practical applications. Corresponding experimental results demonstrate the validity and superiority of the method.

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Section: Joint Motion Model Under Human-exoskeleton Interactionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Gaussian Process-Integrated State Space Model for Continuous Joint Angle Prediction from EMG and Interactive Force in a Human-Exoskeleton System

2019

Self Cite

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“…However, in order to be helpful, devices must provide assistance, and in such cases, transparency has to be reduced. Few studies in the literature investigated the concept of transparency in the framework of a user-centered perspective, being the balance between high transparency and assistance crucial in the process of motor relearning [ 23 – 25 ].…”

Section: Introductionmentioning

confidence: 99%

Assessing User Transparency with Muscle Synergies during Exoskeleton-Assisted Movements: A Pilot Study on the LIGHTarm Device for Neurorehabilitation

Chiavenna

Scano

Malosio

et al. 2018

Applied Bionics and Biomechanics

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Exoskeleton devices for upper limb neurorehabilitation are one of the most exploited solutions for the recovery of lost motor functions. By providing weight support, passively compensated exoskeletons allow patients to experience upper limb training. Transparency is a desirable feature of exoskeletons that describes how the device alters free movements or interferes with spontaneous muscle patterns. A pilot study on healthy subjects was conducted to evaluate the feasibility of assessing transparency in the framework of muscle synergies. For such purpose, the LIGHTarm exoskeleton prototype was used. LIGHTarm provides gravity support to the upper limb during the execution of movements in the tridimensional workspace. Surface electromyography was acquired during the execution of three daily life movements (reaching, hand-to-mouth, and hand-to-nape) in three different conditions: free movement, exoskeleton-assisted (without gravity compensation), and exoskeleton-assisted (with gravity compensation) on healthy people. Preliminary results suggest that the muscle synergy framework may provide valuable assessment of user transparency and weight support features of devices aimed at rehabilitation.

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“…The performance of these exoskeletons is a combination of both the hardware and control system. A wide variety of control systems have been proposed (Aguirre-Ollinger, 2013; Jang et al, 2015; Koller et al, 2015b; Oh et al, 2015; Takahashi et al, 2015; Wu et al, 2015; Yan et al, 2015; Ao et al, 2016; Chen et al, 2016; Ding et al, 2016b; Zhang et al, 2016) but rarely are direct comparisons made. For interested readers, recent reviews of the exoskeleton control literature are available that discuss controllers in detail (Tucker et al, 2015; Yan et al, 2015; Young and Ferris, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Myoelectric control may have advantages over control systems using purely mechanical sensors in that it produces more natural movement and/or faster recognition of the user intent. Research groups have explored myoelectric control for exoskeletons (Andreasen et al, 2005; Ferris et al, 2006; Fleischer et al, 2006; Ao et al, 2016; Chen et al, 2016) and neural control (Gancet et al, 2012; Kilicarslan et al, 2013; Kwak et al, 2014; Soekadar et al, 2015) in the lab. Our goal was to directly compare direct proportional myoelectric control on a hip exoskeleton to a controller that supplies a nominal hip joint torque profile.…”

Section: Introductionmentioning

confidence: 99%

A Biomechanical Comparison of Proportional Electromyography Control to Biological Torque Control Using a Powered Hip Exoskeleton

Young

Gannon

Ferris

2017

Front. Bioeng. Biotechnol.

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BackgroundDespite a large increase in robotic exoskeleton research, there are few studies that have examined human performance with different control strategies on the same exoskeleton device. Direct comparison studies are needed to determine how users respond to different types of control. The purpose of this study was to compare user performance using a robotic hip exoskeleton with two different controllers: a controller that targeted a biological hip torque profile and a proportional myoelectric controller.MethodsWe tested both control approaches on 10 able-bodied subjects using a pneumatically powered hip exoskeleton. The state machine controller targeted a biological hip torque profile. The myoelectric controller used electromyography (EMG) of lower limb muscles to produce a proportional control signal for the hip exoskeleton. Each subject performed two 30-min exoskeleton walking trials (1.0 m/s) using each controller and a 10-min trial with the exoskeleton unpowered. During each trial, we measured subjects’ metabolic cost of walking, lower limb EMG profiles, and joint kinematics and kinetics (torques and powers) using a force treadmill and motion capture.ResultsCompared to unassisted walking in the exoskeleton, myoelectric control significantly reduced metabolic cost by 13% (p = 0.005) and biological hip torque control reduced metabolic cost by 7% (p = 0.261). Subjects reduced muscle activity relative to the unpowered condition for a greater number of lower limb muscles using myoelectric control compared to the biological hip torque control. More subjects subjectively preferred the myoelectric controller to the biological hip torque control.ConclusionMyoelectric control had more advantages (metabolic cost and muscle activity reduction) compared to a controller that targeted a biological torque profile for walking with a robotic hip exoskeleton. However, these results were obtained with a single exoskeleton device with specific control configurations while level walking at a single speed. Further testing on different exoskeleton hardware and with more varied experimental protocols, such as testing over multiple types of terrain, is needed to fully elucidate the potential benefits of myoelectric control for exoskeleton technology.

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Improving the Transparency of an Exoskeleton Knee Joint Based on the Understanding of Motor Intent Using Energy Kernel Method of EMG

Cited by 58 publications

References 33 publications

Gaussian Process-Integrated State Space Model for Continuous Joint Angle Prediction from EMG and Interactive Force in a Human-Exoskeleton System

Gaussian Process-Integrated State Space Model for Continuous Joint Angle Prediction from EMG and Interactive Force in a Human-Exoskeleton System

Assessing User Transparency with Muscle Synergies during Exoskeleton-Assisted Movements: A Pilot Study on the LIGHTarm Device for Neurorehabilitation

A Biomechanical Comparison of Proportional Electromyography Control to Biological Torque Control Using a Powered Hip Exoskeleton

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