A 62,876-parameter attention CNN classifies walking, standing with knee flexion, and sitting with knee extension from four sEMG channels with 85.38% test accuracy on two held-out subjects.
Classification of lower limb electromyographical signals based on autoencoder deep neural network transfer learning,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
other 1
citation-polarity summary
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1roles
other 1polarities
unclear 1representative citing papers
citing papers explorer
-
Attention-Based Convolutional Neural Network Model for Human Lower Limb Activity Recognition using sEMG
A 62,876-parameter attention CNN classifies walking, standing with knee flexion, and sitting with knee extension from four sEMG channels with 85.38% test accuracy on two held-out subjects.