Adaptive Control Design for Human Handwriting Process Based on Electromyography Signals

Imane Mahmoud, Ines Chihi and Afef Abdelkrim

Complexity, 2020, vol. 2020, 1-14

Abstract:

The most used control approaches of hand prosthesis are based on the forearm muscle activities, named ElectroMyoGraphy signal (EMG). In this sense, researchers modeled the hand writing on the plane only from two EMG signals. Based on this analysis, we can consider the hand as a robot with two arms (two degrees of freedom) moving on ( x , y ) plane. However, these signals are very sensitive to many disturbances and are generally unpredictable in time, type, and level. Based on forearm EMG signals, this work aims to propose an adaptive hand-robot control design to generate handwriting. As a first step, we develop the application of the classic proportional integral structure (PI). The PI controller was applied to generate different essays of handwritten graphic traces in one-writer case and multiwriter case. Both cases have presented unsatisfactory results in generating cursive letters and forms. Indeed, we propose, as a second approach, an adaptive PI controller with varying Integral K i gain, according to EMG signals, in order to deal with operation changes.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:5142870

DOI: 10.1155/2020/5142870

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