An electromyogram based force control coordinated in assistive interaction

Noda, Tomoyuki; Furukawa, Jun-ichiro; Teramae, Tatsuya; Hyon, Sang-Ho; Morimoto, Jun

doi:10.1109/icra.2013.6630942

Cited by 21 publications

(18 citation statements)

References 24 publications

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“…Parameter vectors w all and w −i in Equation (7) are derived using the least square method to minimize the error between target torque τ , as in previous studies, [13,17,34] computed from the inverse dynamics of a subject's arm described below and estimated torqueτ :…”

Section: Fault Tolerant Approach For Torque Estimationmentioning

confidence: 99%

“…In this study, we used a one-DOF exoskeleton robot developed in our previous works [13,38] (Figure 2). In a one-DOF system, joint torque τ pam is generated by a pneumatic artificial muscle (PAM): where r is the pulley radius.…”

Section: One-dof Exoskeleton Robotmentioning

confidence: 99%

“…Otherwise we use estimation modelτ all . Since EMG nonlinearity was considered in a previously introduced model (9), here we assume the linear tendonpulley model [13] and estimate the joint torques from the EMG signals:…”

Section: Fault Tolerant Approach For Torque Estimationmentioning

confidence: 99%

“…We then use the derived joint torque profiles to control a one-DOF exoskeleton robot by putting a mannequin arm on the robot. [13] The rest of this paper is organized as follows. In Section 2, we introduce our fault tolerant approach for joint torque estimation.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the following types of EMG-based robot control devices or robots have been investigated: hand prostheses, [1] upper prostheses, [2][3][4] lower prostheses, [5,6] hand exoskeletons, [7][8][9] upper exoskeletons, [10][11][12][13] and lower exoskeletons. [14][15][16][17] For these applications, using multiple sensor channels is a promising approach to estimate user movement intentions.…”

Section: Introductionmentioning

confidence: 99%

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Fault tolerant approach for biosignal-based robot control

Furukawa

Noda²,

Teramae³

et al. 2015

Advanced Robotics

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This paper proposes a fault tolerant framework for biosignal-based robot control with multiple sensor electrodes. In this approach, to cope with sensor faults, a reliable joint torque estimation model is selected from a group of estimation models based on sensor failure classifiers. The correlation among the electromyography (EMG) signal streams is used as input feature vectors for fault detection. To validate our proposed method, we artificially disconnect an EMG electrode or detach one side of an EMG probe from the skin surface during elbow-joint torque estimation experiments with five participants. When one EMG sensor electrode experiences one of the problems, the experimental results show that the joint torque can be estimated with significantly fewer errors using our proposed approach than a joint torque estimation method without sensor fault detection or than a method with a conventional sensor fault detection algorithm. Furthermore, we controlled a mannequin-arm-attached one-DOF exoskeleton based on the estimated torque profiles by generating movements with the estimated torque derived from the selected model.

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Section: Fault Tolerant Approach For Torque Estimationmentioning

confidence: 99%

Section: One-dof Exoskeleton Robotmentioning

confidence: 99%

Section: Fault Tolerant Approach For Torque Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%