Grasping Pattern Recognition and Grasping Force Estimation For Prosthetic Hands

Zhang, Bingke; Zhong, Guoliang; Deng, Hua

doi:10.1051/itmconf/20160709016

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…We compared the performance of the SS model with the MLP and NARX algorithms, which are commonly used in studies with the same goal [13,15,36]. The results shown in Fig.…”

Section: Performance Comparison Of the Ss Model With Other Modelsmentioning

confidence: 99%

Grasping force estimation using state-space model and Kalman filter

Dutra,

Silveira,

Pereira

2021

Preprint

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The grip force required to handle an object depends on it mass and the friction between the skin and the object. The control of grip force in myoelectric prosthesis is crucial for handling objects adequately. The current paper proposes a method for improving the estimation of grip force in myoelectric prosthesis based on surface myoelectric (sEMG) recordings. For this purpose, we develop an approach based on multivariable system identification in the state-space (SS) and continuous force estimation with Kalman filter (KF). The sEMG recordings of ten healthy individuals performing a grip task were used as data set for model identification. The root mean square (RMS), the mean absolute value (MAV), and the waveform length (WL) extracted from the sEMG signals were used at the model's input and the measured grasping force was the output. The performance of the proposed method was evaluated using the normalized root-mean-squared-error (NRMSE) and the square of Pearson's correlation coefficient (R 2 ). In this study, the CC and NRMSE values were 0.92± 0.0319 and 0.723± 0.0563, respectively. The performance of the system was superior to results obtained with a recurrent nonlinear autoregressive exogenous (NARX)-based neural network and the multi-layer perceptron (MLP) network. The results confirmed that the method is an excellent tool for real-time applications with hand prostheses.

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“…We compared the performance of the SS model with the MLP and NARX algorithms, which are commonly used in studies with the same goal [13,15,36]. The results shown in Fig.…”

Section: Performance Comparison Of the Ss Model With Other Modelsmentioning

confidence: 99%

Grasping force estimation using state-space model and Kalman filter

Dutra,

Silveira,

Pereira

2021

Preprint

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The measurement stand with DAQ card for recording a bioelectric signal from human muscles

2019

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The measurement stand for recording a bioelectric signal from human muscles has been described in the paper. A DAQ card with a USB bus has been used in the measuring channel. The acquisition of the electromyography signal is carried out using the non-invasive method using gel electrodes. The bioelectric signal is amplified, filtered and processed in hardware and software. The tests of system operation in the measurement laboratory have been carried out. The presented results relate to the measurement of the electromyography signal from the forearm muscles of the upper limb of a human.

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