Radar Human Motion Recognition Using Motion States and Two-Way Classifications
This work addresses human motion recognition for applications like healthcare or surveillance, but it is incremental as it builds on existing radar-based methods with a state-based framework.
The paper tackles the problem of classifying activities of daily living (ADL) from radar data, particularly for contiguous motions that are inseparable in time, by using motion states and two-way classifiers to improve classification rates compared to using all ADL classes at any given time.
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.