  We consider radar classifications of Activities of Daily Living (ADL) which
can prove beneficial in fall detection, analysis of daily routines, and
discerning physical and cognitive human conditions. We focus on contiguous
motion classifications which follow and commensurate with the human ethogram of
possible motion sequences. Contiguous motions can be closely connected with no
clear time gap separations. In the proposed motion classification approach, we
utilize the Radon transform applied to the radar range-map to detect the
translation motion, whereas an energy detector is used to provide the onset and
offset times of in-place motions, such as sitting down and standing up. It is
shown that motion classifications give different results when performed forward
and backward in time. The number of classes, thereby classification rates,
considered by a classifier, is made variable depending on the current motion
state and the possible transitioning activities in and out of the state. Motion
examples are provided to delineate the performance of the proposed approach
under typical sequences of human motions.
