  The present study introduces a method for improving the classification
performance of imbalanced multiclass data streams from wireless body worn
sensors. Data imbalance is an inherent problem in activity recognition caused
by the irregular time distribution of activities, which are sequential and
dependent on previous movements. We use conditional random fields (CRF), a
graphical model for structured classification, to take advantage of
dependencies between activities in a sequence. However, CRFs do not consider
the negative effects of class imbalance during training. We propose a
class-wise dynamically weighted CRF (dWCRF) where weights are automatically
determined during training by maximizing the expected overall F-score. Our
results based on three case studies from a healthcare application using a
batteryless body worn sensor, demonstrate that our method, in general, improves
overall and minority class F-score when compared to other CRF based classifiers
and achieves similar or better overall and class-wise performance when compared
to SVM based classifiers under conditions of limited training data. We also
confirm the performance of our approach using an additional battery powered
body worn sensor dataset, achieving similar results in cases of high class
imbalance.
