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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Generative AI suitability in qualitative research depends primarily on the approach (small-q positivist/post-positivist or Big Q non-positivist) along with skills, ethics, and personal preferences.
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.
-
To Vibe Research or Not to Vibe Research? Generative AI in Qualitative Research
Generative AI suitability in qualitative research depends primarily on the approach (small-q positivist/post-positivist or Big Q non-positivist) along with skills, ethics, and personal preferences.