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.
Human daily activity recognition for healthcare using wearable and visual sensing data,
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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.