TRR combines multi-band Riemannian features with a GRU to decode high-dimensional finger kinematics from EMG, achieving 9.79° intra-subject and 16.71° cross-subject average absolute error while running at ~10 Hz on a Raspberry Pi.
Pytorch: An imperative style, high-performance deep learning library ,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Decoding High-Dimensional Finger Motion from EMG Using Riemannian Features and RNNs
TRR combines multi-band Riemannian features with a GRU to decode high-dimensional finger kinematics from EMG, achieving 9.79° intra-subject and 16.71° cross-subject average absolute error while running at ~10 Hz on a Raspberry Pi.