  State-of-the-art robotic hand prosthetics generate finger and wrist movement
through pattern recognition (PR) algorithms using features of forearm
electromyogram (EMG) signals, but re- quires extensive training and is prone to
poor predictions for conditions outside the training data (Peerdeman et al.,
2011; Scheme et al., 2010). We propose a novel approach to develop a dynamic
robotic limb by utilizing the recent history of EMG signals in a model that
accounts for physiological features of hand movement which are ignored by PR
algorithms. We do this by viewing EMG signals as functional covariates and
develop a functional linear model that quantifies the effect of the EMG signals
on finger/wrist velocity through a bivariate coefficient function that is
allowed to vary with current finger/wrist position. The model is made par-
simonious and interpretable through a two-step variable selection procedure,
called Sequential Adaptive Functional Empirical group LASSO (SAFE-gLASSO).
Numerical studies show excel- lent selection and prediction properties of
SAFE-gLASSO compared to popular alternatives. For our motivating dataset, the
method correctly identifies the few EMG signals that are known to be important
for an able-bodied subject with negligible false positives and the model can be
directly implemented in a robotic prosthetic.
