  The past decade has seen an increased interest in human activity recognition.
Most commonly, the raw data coming from sensors attached to body parts are
unannotated, which creates a need for fast labelling method. Part of the
procedure is choosing or designing an appropriate performance measure. We
propose a new performance measure, the Locally Time-Shifted Measure, which
addresses the issue of timing uncertainty of state transitions in the
classification result. Our main contribution is a novel post-processing method
for binary activity recognition. It improves the accuracy of the classification
methods, by correcting for unrealistically short activities in the estimate.
