Estimation of Finger Joint Angles from sEMG Using a Neural Network Including Time Delay Factor and Recurrent Structure

<table>Structure of the estimating system. Subscript <svg height="10.7375" id="M4" style="vertical-align:-0.13794pt" version="1.1" viewbox="0 0 8.6000004 10.7375" width="8.6000004" xmlns="http://www.w3.org/2000/svg">
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<tspan style="font-size: 12.50px; " x="0" y="0">𝑘</tspan>
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</svg> indicates <svg height="10.975" id="M5" style="vertical-align:-0.13794pt" version="1.1" viewbox="0 0 20.875 10.975" width="20.875" xmlns="http://www.w3.org/2000/svg">
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<g transform="translate(72,-63.22)">
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<tspan style="font-size: 12.50px; " x="0" y="0">𝑘</tspan>
<tspan style="font-size: 12.50px; " x="6.7766261" y="0">t</tspan>
<tspan style="font-size: 12.50px; " x="10.339981" y="0">h</tspan>
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</svg>-step data. The main part of this estimating system is the neural network (NN). The input vector into the NN has feedforward and feedback components.</table>

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Figure 2

Figure 2: Estimation of Finger Joint Angles from sEMG Using a Neural Network Including Time Delay Factor and Recurrent Structure 