  We perform classification of activities of daily living (ADL) using a
Frequency-Modulated Continuous Waveform (FMCW) radar. In particular, we
consider contiguous motions that are inseparable in time. Both the
micro-Doppler signature and range-map are used to determine transitions from
translation (walking) to in-place motions and vice versa, as well as to provide
motion onset and the offset times. The possible classes of activities post and
prior to the translation motion can be separately handled by forward and
background classifiers. The paper describes ADL in terms of states and
transitioning actions, and sets a framework to deal with separable and
inseparable contiguous motions. It is shown that considering only the
physically possible classes of motions stemming from the current motion state
improves classification rates compared to incorporating all ADL for any given
time.
