  Stroke rehabilitation seeks to increase neuroplasticity through the repeated
practice of functional motions, but may have minimal impact on recovery because
of insufficient repetitions. The optimal training content and quantity are
currently unknown because no practical tools exist to measure them. Here, we
present PrimSeq, a pipeline to classify and count functional motions trained in
stroke rehabilitation. Our approach integrates wearable sensors to capture
upper-body motion, a deep learning model to predict motion sequences, and an
algorithm to tally motions. The trained model accurately decomposes
rehabilitation activities into component functional motions, outperforming
competitive machine learning methods. PrimSeq furthermore quantifies these
motions at a fraction of the time and labor costs of human experts. We
demonstrate the capabilities of PrimSeq in previously unseen stroke patients
with a range of upper extremity motor impairment. We expect that these advances
will support the rigorous measurement required for quantitative dosing trials
in stroke rehabilitation.
