  We propose MetaEMG, a meta-learning approach for fast adaptation in intent
inferral on a robotic hand orthosis for stroke. One key challenge in machine
learning for assistive and rehabilitative robotics with disabled-bodied
subjects is the difficulty of collecting labeled training data. Muscle tone and
spasticity often vary significantly among stroke subjects, and hand function
can even change across different use sessions of the device for the same
subject. We investigate the use of meta-learning to mitigate the burden of data
collection needed to adapt high-capacity neural networks to a new session or
subject. Our experiments on real clinical data collected from five stroke
subjects show that MetaEMG can improve the intent inferral accuracy with a
small session- or subject-specific dataset and very few fine-tuning epochs. To
the best of our knowledge, we are the first to formulate intent inferral on
stroke subjects as a meta-learning problem and demonstrate fast adaptation to a
new session or subject for controlling a robotic hand orthosis with EMG
signals.
