  In this paper, we introduce a shape-based, time-scale invariant feature
descriptor for 1-D sensor signals. The time-scale invariance of the feature
allows us to use feature from one training event to describe events of the same
semantic class which may take place over varying time scales such as walking
slow and walking fast. Therefore it requires less training set. The descriptor
takes advantage of the invariant location detection in the scale space theory
and employs a high level shape encoding scheme to capture invariant local
features of events. Based on this descriptor, a scale-invariant classifier with
"R" metric (SIC-R) is designed to recognize multi-scale events of human
activities. The R metric combines the number of matches of keypoint in scale
space with the Dynamic Time Warping score. SICR is tested on various types of
1-D sensors data from passive infrared, accelerometer and seismic sensors with
more than 90% classification accuracy.
