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This research aims to evaluate various AI approaches for classifying 1D EMG signals while optimizing signal acquisition parameters.

April 17, 2026BioengineeringOpen Access

Optimized Signal Acquisition and Advanced AI for Robust 1D EMG Classification: A Comparative Study of Machine Learning, Deep Learning, and Reinforcement Learning

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This research aims to evaluate various AI approaches for classifying 1D EMG signals while optimizing signal acquisition parameters.
Conducted a comparative analysis of ML, DL, and RL models for EMG classification.
Utilized both synthetic and real-world EMG datasets.
Optimized signal acquisition using 8-10 bit quantization and a 2000 Hz sampling rate.
Evaluated performance of models, including ensemble methods and advanced DL architectures.
Ensemble methods achieved 100% accuracy on real EMG data.
Advanced DL architectures, including LSTM and Transformer, reached 96.3% accuracy.
Reinforcement learning agents demonstrated 100% accuracy on multiclass synthetic data.
Optimized preprocessing significantly improved EMG classification accuracy.

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Comparative analysis shows improved EMG classification using advanced AI techniques, implying better prosthetic control.

Shinde et al. (2026) studied this question.

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