Estimating Deficient Muscle Activity using LSTM with Integrated Damping Neurons for EMG-Based Control of Robotic Prosthetic Fingers

نویسندگان

چکیده

Robotic prosthetic hands can help perform the intended sophisticated movements of upper limb, which assist amputees to their daily activities. Although a robotic hand be controlled in real-time using user’s electromyography (EMG), directly reflects motion intention, some important EMG signals are usually lost owing muscle deficiency. This study proposes activity estimator that is inspired by synergy across subjects estimate missing muscles real-time. The proposed learns from balance, finger joint angles, and grasping force healthy persons. developed as an artificial neural network (ANN) with novel cell structure combines long-short-term memory damping neurons analyze dynamics. Furthermore, improve accuracy learning synergy, input selected focusing on enslavement anatomical relationships. effectiveness evaluated experiments. results showed contribute well realization motions user.

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ژورنال

عنوان ژورنال: IEEE Access

سال: 2023

ISSN: ['2169-3536']

DOI: https://doi.org/10.1109/access.2023.3312575