Radar Human Motion Recognition Using Motion States and Two-Way Classifications

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 offset times. The in-place motion classes post and prior to walking can be separately handled by forward and backward in time classifiers. The paper describes ADL in terms of motion states and transitioning motions or activities, and sets forward a framework to deal with time separable and inseparable activities. It is shown that limiting the classes of activities, which are physically possible and associated with the current state, improves the classification rates compared to incorporating all ADL for any given time.