A Power Assist Device Based on Joint Equilibrium Point Estimation from EMG Signals

Kawase, Toshihiro; Kambara, Hiroyuki; Koike, Yasuharu

doi:10.20965/jrm.2012.p0205

Cited by 24 publications

(23 citation statements)

References 17 publications

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“…Current rehabilitation robots can perform sophisticated operations including stiffness control [ 32 , 33 ]. The human musculoskeletal system has stiffness and viscosity properties essential to interaction with our surroundings.…”

Section: Discussionmentioning

confidence: 99%

Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate

Shin

Kambara

Yoshimura

et al. 2018

Computational Intelligence and Neuroscience

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Electrocorticogram (ECoG) is a well-known recording method for the less invasive brain machine interface (BMI). Our previous studies have succeeded in predicting muscle activities and arm trajectories from ECoG signals. Despite such successful studies, there still remain solving works for the purpose of realizing an ECoG-based prosthesis. We suggest a neuromuscular interface to control robot using decoded muscle activities and joint angles. We used sparse linear regression to find the best fit between band-passed ECoGs and electromyograms (EMG) or joint angles. The best coefficient of determination for 100 s continuous prediction was 0.6333 ± 0.0033 (muscle activations) and 0.6359 ± 0.0929 (joint angles), respectively. We also controlled a 4 degree of freedom (DOF) robot arm using only decoded 4 DOF angles from the ECoGs in this study. Consequently, this study shows the possibility of contributing to future advancements in neuroprosthesis and neurorehabilitation technology.

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Section: Discussionmentioning

confidence: 99%

Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate

Shin

Kambara

Yoshimura

et al. 2018

Computational Intelligence and Neuroscience

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“…The primary advantage of the proposed method is that it can predict muscle activities and joint angle during sequential movement tasks. If we can predict muscle activities, joint torque and stiffness can also be predicted using previously proposed methods [47,48]. This creates remarkable benefits, which would contribute to the realization of ECoG-based prosthetics.…”

Section: Resultsmentioning

confidence: 99%

Decoding of Kinetic and Kinematic Information from Electrocorticograms in Sensorimotor Cortex: A Review

Nakanishi¹

2014

Int J Neurorehabilitation Eng

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IntroductionBrain-machine interfaces (BMI) are useful technologies to provide assistance to disabled individuals, allowing them interaction with their environments. A number of prominent brain-machine interface studies have arisen over the past two decades. These BMI systems translate brain signals into commands for controlling devices such as cursors [1], spelling devices [2], and neural prosthetics [3][4][5][6][7][8][9]. This new communication has not only the potential to help to disabled persons but also provide insight into the motor system of the brain [10][11][12][13][14].Several sensors have been developed to measure brain signals. These are mainly categorized into two types, invasive sensor i.e. intracortical microelectrodes and non-invasive sensors such as electroencephalography (EEG) and magneto encephalography. Lots of invasive BMI studies have successfully demonstrated prosthetic devices [6][7][8][9]. However, they have the risk such as brain injury. Since EEG are non-invasive and have high temporal resolution, previous works have developed such as online cursor control [15], direction of hand movements [16,17], a spelling device [18], and neuro feedback for rehabilitation [19,20]. Although a large number of these non-invasive works succeeded in classification of movement intention, prediction of time-varying trajectories is difficult due to insufficient spatial resolution and low signal-to-noise ratio in such methods.Electrocorticography (ECoG) is an alternative approach to less invasive BMIs [21][22][23][24][25][26][27][28][29]. ECoG is a technique that measures electrical activity in the cerebral cortex by means of electrodes placed directly on the surface of the brain. Compared to EEG, ECoG has higher spatio-temporal resolution with better signal-to-noise ratio than scalp EEG [30,31]. ECoG has also shown potential as a stable longterm recording method [27]. Several studies using ECoG have already succeeded in the classification of movement direction [22,23], grasp type [28], and prediction of hand trajectory [24,26,27], and decoding of hand trajectories [25,27,32], arm trajectories [33] and finger movement [34,35]. Predictions of muscle activities from ECoG signals during reaching and grasping movements in monkeys have also been successful [36]. Despite these successes, however, there still remains considerable work for the realization of ECoG-based neuroprosthesis. Since the human neuromuscular system naturally modulates mechanical stiffness and viscosity to achieve proper interaction with the environment, we have not only decoded kinematic information such as trajectory but also kinetic information such as torque, stiffness AbstractBrain-machine interface techniques have been applied in a number of studies to control neuromotor prostheses and for neuro-rehabilitation in the hopes of providing a means to restore lost motor function. Electrocorticography has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive electroencephalography and is less in...

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“…Calculated by equation (5), the resultant force in toe is 1370.97 N, the resultant force in heel is 1254.34 N. In order to avoid the tension of the wire rope destroying the shafts, the diameters of toe and heel shafts are 4 mm and 6 mm. The detailed information for these structural parts is shown in Table.2.…”

Section: Calculated By Equationmentioning

confidence: 99%

“…The EMG signals of ankle muscles during push-recover process show that the output torque and rotation angle of ankle joints have an approximate linear relationship, but there are still significant deviations [5]. The joint proprioception, vestibular organs in the inner ear, and vision information are transferred to the network of neurons center for decision of the behavior of ankle joint [6].…”

Section: Introductionmentioning

confidence: 99%

Design and Reflex Control for a Series Elastic Actuator Based Ankle Joint Emulator

Pang

Xiang

et al. 2018

2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM)

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Human body is a highly efficient stable system, although the center of mass is high. While quiet standing ankle joint has the function of resisting disturbance to maintain balance of human body. Studying the control mechanism of ankle joint helps to improve relative technologies such as bipedal robot, powered prosthesis and exoskeleton. In this paper, a series elastic actuator (SEA) based ankle joint emulator and reflex-like control algorithm are purposed to test human push-recovery mechanism. The SEA element provides an easy way for ankle joint torque control. Proportion-Derivative (PD) based reflex-like control algorithm calculates reference torque for SEA element. Experimental results indicate that this proposed emulator has the ability to mimic human ankle joint quiet standing push-recovery performance. The emulator is a useful platform to test further human ankle joint torque control function.

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A Power Assist Device Based on Joint Equilibrium Point Estimation from EMG Signals

Cited by 24 publications

References 17 publications

Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate

Control of a Robot Arm Using Decoded Joint Angles from Electrocorticograms in Primate

Decoding of Kinetic and Kinematic Information from Electrocorticograms in Sensorimotor Cortex: A Review

Design and Reflex Control for a Series Elastic Actuator Based Ankle Joint Emulator

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