It would be valuable if home-based rehabilitation training technologies could automatically assess arm impairment after stroke. Here, we tested whether a simple measure—the repetition rate (or “rep rate”) when performing specific exercises as measured with simple sensors—can be used to estimate Upper Extremity Fugl-Meyer (UEFM) score.
41 individuals with arm impairment after stroke performed 12 sensor-guided exercises under therapist supervision using a commercial sensor system comprised of two pucks that use force and motion sensing to measure the start and end of each exercise repetition. 14 of these participants then used the system at home for three weeks.
Using linear regression, UEFM score was well estimated using the rep rate of one forward-reaching exercise from the set of 12 exercises (r2 = 0.75); this exercise required participants to alternately tap pucks spaced about 20 cm apart (one proximal, one distal) on a table in front of them. UEFM score was even better predicted using an exponential model and forward-reaching rep rate (Leave One Out Cross Validation (LOOCV) r2 = 0.83). We also tested the ability of a nonlinear, multivariate model (a regression tree) to predict UEFM, but such a model did not improve prediction (LOOCV r2 = 0.72). However, the optimal decision tree also used the forward-reaching task along with a pinch grip task to subdivide more and less impaired patients in a way consistent with clinical intuition. At home, rep rate for the forward-reaching exercise well predicted UEFM score using an exponential model (LOOCV r2 = 0.69), but only after we re-estimated coefficients using the home data.
These results show how a simple measure—exercise rep rate measured with simple sensors—can be used to infer an arm impairment score and suggest that prediction models should be tuned separately for the clinic and home environments.