Myoelectric grip force prediction using deep learning for hand robot

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Deep learning models predicted grip strength in robotic hands, highlighting AI's potential for user intention control.

CNN models predicted grip strength with a root mean square error of 2.35 N, illustrating their accuracy in robotic applications.
The evaluation compared CNN architectures against state-of-the-art regression methods, demonstrating superior predictive power.
Electromyographic signals and dynamometer data provided training input, establishing a foundation for deep learning models.
This work supports advancements in assistive technologies, emphasizing the role of AI in enhancing robotic control systems.

Anam et al. (2025) studied this question.

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