Decoding Hand Actions Through Signal Analysis: Advancements in Prosthetic Limb Control
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Abstract
The intricate nature of human hand movements presents a significant challenge in the development of intuitive and dexterous prosthetic limbs. This article explores the critical role of signal analysis, particularly focusing on electromyography (EMG), in deciphering the complex patterns associated with various hand activities. By examining recent advancements in signal acquisition, feature extraction, and machine learning algorithms, we highlight the implications of these techniques for enhancing the control and functionality of prosthetic hands. This review synthesizes current research, identifies key trends, and discusses future directions aimed at creating more seamless and naturalistic prosthetic control systems.
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