Intention-Based EMG Control for Powered Exoskeletons

Lenzi, Tommaso; Rossi, Stefano Marco Maria De; Vitiello, Nicola; Carrozza, Maria Chiara

doi:10.1109/tbme.2012.2198821

Cited by 347 publications

(227 citation statements)

References 54 publications

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“…Due to the many factors that modify the EMG signals, this type of control requires a complex calibration processes to adapt to the variability of the signals, and depends on the user and the session conditions. In the experimental work presented in [23], the reactions of ten healthy subjects to the assistance provided through a proportional EMG control applied by an elbow powered exoskeleton is studied. The system did not require calibration.…”

Section: Related Workmentioning

confidence: 99%

A High-Level Control Algorithm Based on sEMG Signalling for an Elbow Joint SMA Exoskeleton

Copaci¹,

Serrano²,

Moreno³

et al. 2018

Preprint

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Abstract:A high-level control algorithm capable of generating position and torque references from surface electromyography signals (sEMG) has been designed. It is applied to a shape memory alloy (SMA) actuated exoskeleton used in active rehabilitation therapies for elbow joints. The sEMG signals are filtered and normalized according data collected online during the first seconds of therapy sessions. The control algorithm uses the sEMG signals to promote active participation of patients during the therapy session. In order to generate the position reference pattern with good precision, the sEMG normalized signal is compared with a pressure sensor signal to detect the intention of each movement. The algorithm has been tested in simulations and with healthy people for control of an elbow exoskeleton in flexion-extension movements. The results indicate that sEMG signals from elbow muscles in combination with pressure sensors that measure arm-exoskeleton interaction can be used as inputs for the control algorithm, which adapts the reference for exoskeleton movements according a patient's intention.

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Section: Related Workmentioning

confidence: 99%

A High-Level Control Algorithm Based on sEMG Signalling for an Elbow Joint SMA Exoskeleton

Copaci¹,

Serrano²,

Moreno³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Correlations among the data representing an event are usually reflected in multiple measurements at different locations [5,6]. The 2 Mobile Information Systems acquisition of electroencephalogram, electromyography, and other biological signals have become a hot research topic in the field of human-machine interaction owing to their quick response [6,7]. However, the measurement of biological signals involves poor robustness because of their low frequency, weak amplitude, and low signal-to-noise ratio [8].…”

Section: Introductionmentioning

confidence: 99%

Design and Voluntary Motion Intention Estimation of a Novel Wearable Full-Body Flexible Exoskeleton Robot

Chen

Wang

Liu

et al. 2017

Mobile Information Systems

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The wearable full-body exoskeleton robot developed in this study is one application of mobile cyberphysical system (CPS), which is a complex mobile system integrating mechanics, electronics, computer science, and artificial intelligence. Steel wire was used as the flexible transmission medium and a group of special wire-locking structures was designed. Additionally, we designed passive joints for partial joints of the exoskeleton. Finally, we proposed a novel gait phase recognition method for full-body exoskeletons using only joint angular sensors, plantar pressure sensors, and inclination sensors. The method consists of four procedures. Firstly, we classified the three types of main motion patterns: normal walking on the ground, stair-climbing and stair-descending, and sit-to-stand movement. Secondly, we segregated the experimental data into one gait cycle. Thirdly, we divided one gait cycle into eight gait phases. Finally, we built a gait phase recognition model based on -Nearest Neighbor perception and trained it with the phase-labeled gait data. The experimental result shows that the model has a 98.52% average correct rate of classification of the main motion patterns on the testing set and a 95.32% average correct rate of phase recognition on the testing set. So the exoskeleton robot can achieve human motion intention in real time and coordinate its movement with the wearer.

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“…On one hand, to realize the robot-aided rehabilitation training, a human-robot interface must be established to help the robot understand the motion intention of the operator. 4 On the other hand, the robotic assistance should be adjustable to meet the needs of patients in different rehabilitation stages.…”

Section: Introductionmentioning

confidence: 99%

A Subject-Specific EMG-Driven Musculoskeletal Model for Applications in Lower-Limb Rehabilitation Robotics

Ding

Liu

et al. 2016

Int. J. Human. Robot.

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Robotic devices have great potential in physical therapy owing to their repeatability, reliability and cost economy. However, there are great challenges to realize active control strategy, since the operator's motion intention is uneasy to be recognized by robotics online. The purpose of this paper is to propose a subject-specific electromyography (EMG)-driven musculoskeletal model to estimate subject's joint torque in real time, which can be used to detect his/her motion intention by forward dynamics, and then to explore its potential applications in rehabilitation robotics control. The musculoskeletal model uses muscle activation dynamics to extract muscle activation from raw EMG signals, a Hill-type muscle-tendon model to calculate muscle contraction force, and a proposed subject-specific musculoskeletal geometry model to calculate muscular moment arm. The parameters of muscle activation dynamics and muscle-tendon model are identified by off-line optimization methods in order to minimize the differences between the estimated muscular torques and the reference torques. Validation experiments were conducted on six healthy subjects to evaluate the proposed model. Experimental results demonstrated the model's ability to predict knee joint torque in high-accuracy with the coefficient of determination ( 2 R ) value of 0.934 0.013  and the normalized root-mean-square error (RMSE) of 11.58% 1.44%  .

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Intention-Based EMG Control for Powered Exoskeletons

Cited by 347 publications

References 54 publications

A High-Level Control Algorithm Based on sEMG Signalling for an Elbow Joint SMA Exoskeleton

A High-Level Control Algorithm Based on sEMG Signalling for an Elbow Joint SMA Exoskeleton

Design and Voluntary Motion Intention Estimation of a Novel Wearable Full-Body Flexible Exoskeleton Robot

A Subject-Specific EMG-Driven Musculoskeletal Model for Applications in Lower-Limb Rehabilitation Robotics

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