A Prosthesis Control System Based on the Combination of Speech and sEMG Signals and Its Performance Assessment

Wei, Zheng; Peng, Fang; Lan, Tian; Zhuo, Qiongfang; Li, Guanglin

doi:10.1007/978-3-319-06269-3_9

Cited by 1 publication

(1 citation statement)

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“…Gaudet et al [18] achieved the classification of upper limb phantom movements in transhumeral amputees using electromyographic and kinematic features. Previous scholars have proven that sEMG signals of residual limbs of amputation patients could be used for motion intention recognition, which verified the feasibility of sEMG signals in motion control for the prosthesis [19][20][21]. The muscles on the thigh stump could also be activated when amputee moves.…”

Section: Introductionmentioning

confidence: 81%

Research on Joint-Angle Prediction Based on Artificial Neural Network for Above-Knee Amputees

Yang

Zhao

et al. 2021

Sensors

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In the current study, our research group proposed an asymmetric lower extremity exoskeleton to enable above-knee amputees to walk with a load. Due to the absence of shank and foot, the knee and ankle joint at the amputation side of the exoskeleton lack tracking targets, so it is difficult to realize the function of assisted walking when going up and downstairs. Currently, the use of lower-limb electromyography to predict the angles of lower limb joints has achieved remarkable results. However, the prediction effect was poor when only using electromyography from the thigh. Therefore, this paper introduces hip-angle and plantar pressure signals for improving prediction effect and puts forward a joint prediction method of knee- and ankle-joint angles by electromyography of the thigh, hip-joint angle, and plantar pressure signals. The generalized regression neural network optimized by the golden section method is used to predict the joint angles. Finally, the parameters (the maximum error, the Root-Mean-Square error (RMSE), and correlation coefficient (γ)) were calculated to verify the feasibility of the prediction method.

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Section: Introductionmentioning

confidence: 81%