- University of British Columbia
We present a multi-frequency feature set to detect driver's 3D head and torso movements from fluctuations in the Radio Frequency (RF) channel due to body movements. Current features used for movement detection are based on time-of-flight, received signal strength and channel state information, and come with the limitations of coarse tracking, sensitivity towards multipath effects and handling corrupted phase data, respectively. There is no standalone feature set which accurately detects small and large movements and determines the direction in 3D space. We resolve this problem by using two radio signals at widely separated frequencies in a monostatic configuration. By combining information about displacement, velocity, and direction of movements derived from the Doppler Effect at each frequency, we expand the number of existing features. We separate Pitch, Roll, and Yaw movements of head from torso and arm. The extracted feature set is used to train a K-Nearest Neighbor classification algorithm which could provide behavioral awareness to cars while being less invasive as compared to camera-based systems. The training results on a data from 4 participants reveal that at 1.8GHz, the classification accuracy is 77.4%, at 30GHz it is 87.4%, and multi-frequency feature set improves the accuracy to 92%.
|Number of pages||5|
|Journal||IEEE Antennas and Wireless Propagation Letters|
|Early online date||2019|
|Publication status||Published - 1 May 2019|
|MoE publication type||A1 Journal article-refereed|
- Torso, Feature extraction, Doppler effect, Three-dimensional displays, Sensors, Radio frequency, Time-frequency analysis, Class membership, dynamic features, doppler Effect, multi-frequency, wireless sensors, 3D posture recognition