  Radar for indoor monitoring is an emerging area of research and development,
covering and supporting different health and wellbeing applications of smart
homes, assisted living, and medical diagnosis. We report on a successful RF
sensing system for home monitoring applications. The system recognizes
Activities of Daily Living(ADL) and detects unique motion characteristics,
using data processing and training algorithms. We also examine the challenges
of continuously monitoring various human activities which can be categorized
into translation motions (active mode) and in-place motions (resting mode). We
use the range-map, offered by a range-Doppler radar, to obtain the transition
time between these two categories, characterized by changing and constant range
values, respectively. This is achieved using the Radon transform that
identifies straight lines of different slopes in the range-map image. Over the
in-place motion time intervals, where activities have insignificant or
negligible range swath, power threshold of the radar return micro-Doppler
signatures,which is employed to define the time-spans of individual activities
with insignificant or negligible range swath. Finding both the transition times
and the time-spans of the different motions leads to improved classifications,
as it avoids decisions rendered over time windows covering mixed activities.
