We consider Activities of Daily Living (ADL) as actions that are continuously monitored and cannot easily be separated in time. Both, the micro-Doppler signature and range-map are used to determine transitions from translation (walking) to in-place activities and vice versa, as well as to provide activity onset and offset times. The in-place action classes after and before walking are handled by forward and backward in time classifiers. The paper extends the recently published work on ADL based human states and transitioning activities, where time separable and inseparable activities were exploited. The previous work has shown that limiting the classes of activities, which are physically possible and associated with the current state, improves the classification rates compared to considering all possible ADL in the dataset at any given time. These approaches are suitable for switching classification methods to achieve superior classification results.