Decoding gestures from the upper limb using noninvasive surface
electromyogram (sEMG) signals is of keen interest for the rehabilitation of
amputees, artificial supernumerary limb augmentation, gestural control of
computers, and virtual/augmented realities. We show that sEMG signals recorded
across an array of sensor electrodes in multiple spatial locations around the
forearm evince a rich geometric pattern of global motor unit (MU) activity that
can be leveraged to distinguish different hand gestures. We demonstrate a
simple technique to analyze spatial patterns of muscle MU activity within a
temporal window and show that distinct gestures can be classified in both
supervised and unsupervised manners. Specifically, we construct symmetric
positive definite (SPD) covariance matrices to represent the spatial
distribution of MU activity in a time window of interest, calculated as
pairwise covariance of electrical signals measured across different electrodes.
This allows us to understand and manipulate multivariate sEMG timeseries on a
more natural subspace -the Riemannian manifold. Furthermore, it directly
addresses signal variability across individuals and sessions, which remains a
major challenge in the field. sEMG signals measured at a single electrode lack
contextual information such as how various anatomical and physiological factors
influence the signals and how their combined effect alters the evident
interaction among neighboring muscles. As we show here, analyzing spatial
patterns using covariance matrices on Riemannian manifolds allows us to
robustly model complex interactions across spatially distributed MUs and
provides a flexible and transparent framework to quantify differences in sEMG
signals across individuals. The proposed method is novel in the study of sEMG
signals and its performance exceeds the current benchmarks while maintaining
exceptional computational efficiency.Comment: 15 pages, 8 figures, 5 table