  Compared to in-clinic balance training, in-home training is not as effective.
This is, in part, due to the lack of feedback from physical therapists (PTs).
Here, we analyze the feasibility of using trunk sway data and machine learning
(ML) techniques to automatically evaluate balance, providing accurate
assessments outside of the clinic. We recruited sixteen participants to perform
standing balance exercises. For each exercise, we recorded trunk sway data and
had a PT rate balance performance on a scale of 1 to 5. The rating scale was
adapted from the Functional Independence Measure. From the trunk sway data, we
extracted a 61-dimensional feature vector representing performance of each
exercise. Given these labeled data, we trained a multi-class support vector
machine (SVM) to map trunk sway features to PT ratings. Evaluated in a
leave-one-participant-out scheme, the model achieved a classification accuracy
of 82%. Compared to participant self-assessment ratings, the SVM outputs were
significantly closer to PT ratings. The results of this pilot study suggest
that in the absence of PTs, ML techniques can provide accurate assessments
during standing balance exercises. Such automated assessments could reduce PT
consultation time and increase user compliance outside of the clinic.
