Human Movement Modeling to Detect Biosignal Sensor Failures for Myoelectric Assistive Robot Control

Furukawa, Jun-ichiro; Noda, Tomoyuki; Teramae, Tatsuya; Morimoto, Jun

doi:10.1109/tro.2017.2683522

Cited by 27 publications

(20 citation statements)

References 33 publications

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“…The first area yet to be investigated in a clinical trial is a comparative study between an end-effector robotic system and an exoskeleton robotic system. Both end-effector robot [14,[79][80][81][82][83] and exoskeleton robot [24,43,46,47,52,61,62,64] have shown potential to reduce impairments and it is difficult to compare their result as both operate differently. A comprehensive clinical study is required to identify the potential benefits of one device over the other in reducing impairment and improvement in motor function.…”

Section: Discussionmentioning

confidence: 99%

“…The robotic systems used for upper limb rehabilitation can be studied based on their mechanical structure, control system, and clinical applications. The mechanical configuration [8,[19][20][21][22][23][24][25][26][27]and control systems [28][29][30][31][32][33][34][35][36] have been reviewed previously. A detailed insight on various end effector based system and their application in stroke rehabilitation have also been carried out [37].…”

mentioning

confidence: 99%

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Upper limb rehabilitation using robotic exoskeleton systems: a systematic review

Rehmat

Zuo

Meng

et al. 2018

Int J Intell Robot Appl

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Upper limb rehabilitation using robotic exoskeleton systems: a systematic review

Rehmat

Zuo

Meng

et al. 2018

Int J Intell Robot Appl

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“…We used the upper limb exoskeleton robot in this study 7 , 8 . The link lengths are 0.265 m from the shoulder to the elbow joint and 0.26 m from the elbow to the wrist, and the upper limb exoskeleton robot has four degrees-of-freedom: shoulder flexion/extension, shoulder abduction/adduction, elbow flexion/extension, and wrist flexion/extension joints.…”

Section: Methodsmentioning

confidence: 99%

“…To this end, we used the exoskeleton robot 7 , 8 instead of an end-effector robot. The exoskeleton is placed on the user’s entire body, just the upper or lower extremities, or even a specific body segment, such as the elbow or ankle.…”

Section: Introductionmentioning

confidence: 99%

Passive training with upper extremity exoskeleton robot affects proprioceptive acuity and performance of motor learning

Chiyohara

Furukawa

Noda

et al. 2020

Sci Rep

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Sports trainers often grasp and move trainees' limbs to give instructions on desired movements, and a merit of this passive training is the transferring of instructions via proprioceptive information. However, it remains unclear how passive training affects the proprioceptive system and improves learning. This study examined changes in proprioceptive acuity due to passive training to understand the underlying mechanisms of upper extremity training. Participants passively learned a trajectory of elbow-joint movement as per the instructions of a single-arm upper extremity exoskeleton robot, and the performance of the target movement and proprioceptive acuity were assessed before and after the training. We found that passive training improved both the reproduction performance and proprioceptive acuity. We did not identify a significant transfer of the training effect across arms, suggesting that the learning effect is specific to the joint space. Furthermore, we found a significant improvement in learning performance in another type of movement involving the trained elbow joint. These results suggest that participants form a representation of the target movement in the joint space during the passive training, and intensive use of proprioception improves proprioceptive acuity.

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“…It is an alternative to reflect the patient's voluntary effort. The training involving the patient's voluntary effort can improve the therapeutic effect [5]. The surface electromyography (sEMG) is a non-invasive EMG signal collected by surface electrodes, which has the advantage of being more accessible than electroencephalography (EEG) and less likely to impede the user's normal activities.…”

Section: Introductionmentioning

confidence: 99%

Joint Torque Closed-Loop Estimation Using NARX Neural Network Based on sEMG Signals

Chen

Yang

et al. 2020

IEEE Access

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Joint torque estimation is of great significance for the research and clinical application of intelligent rehabilitation technology. This paper proposes a closed-loop model for joint torque estimation based on surface electromyography (sEMG). Combined with the physiological characteristics of muscle activation, a nonlinear autoregressive with eXogenous inputs(NARX) neural network model for joint torque estimation based on sEMG signals is established. In order to solve the drift phenomenon of torque estimation, a state-space framework is constructed by regarding NARX neural network based torque estimation model as state model and developing a measurement model by the easily measured joint angle signals. With the built state-space model, the extended Kalman filter (EKF) is used to realize the closedloop filtering of the estimated torque. In order to test the accuracy of the proposed closed-loop joint torque estimation, 8 volunteers were recruited to perform elbow joint isotonic motion experiments under four kinds of loads. The test results show that the average normalized root mean square error (NRMSE) between the estimated values of closed-loop model and measurement values of all subjects under load-dependent, multiload and load-independent tests are 0.1080±0.0411, 0.1326±0.0494 and 0.1674±0.0661 respectively, which is significant better than the results of the open-loop model (0.2694±0.1584 (p < 10 −6), 0.2499±0.1326 (p < 10 −6) and 0.3435±0.2061 (p < 10 −4)). The presented closed-loop model combines offline modeling and online filtering to achieve online estimation of joint torque, which ameliorates the problem that the estimated torque in the open-loop model deviates greatly from the actual values and improves the accuracy of joint torque estimation. INDEX TERMS sEMG, neural network, NARX, EKF.

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Human Movement Modeling to Detect Biosignal Sensor Failures for Myoelectric Assistive Robot Control

Cited by 27 publications

References 33 publications

Upper limb rehabilitation using robotic exoskeleton systems: a systematic review

Upper limb rehabilitation using robotic exoskeleton systems: a systematic review

Passive training with upper extremity exoskeleton robot affects proprioceptive acuity and performance of motor learning

Joint Torque Closed-Loop Estimation Using NARX Neural Network Based on sEMG Signals

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