Wireless Multi-frequency Feature Set to Simplify Human 3D Pose Estimation. Raja, M., Hughes, A., Xu, Y., zarei , P., Michelson, D. G., & Sigg, S. IEEE Antennas and Wireless Propagation letters, 18(5):876-880, 2019.
doi  abstract   bibtex   
We present a multifrequency feature set to detect driver's three-dimensional (3-D) head and torso movements from fluctuations in the radio frequency channel due to body movements. Current features used for movement detection are based on the time-of-flight, received signal strength, and channel state information and come with the limitations of coarse tracking, sensitivity toward multipath effects, and handling corrupted phase data, respectively. There is no standalone feature set that accurately detects small and large movements and determines the direction in 3-D 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 data from four participants reveal that the classification accuracy is 77.4% at 1.8 GHz, it is 87.4% at 30 GHz, and multifrequency feature set improves the accuracy to 92%.
@article{Raja_2019_antenna,
author={Muneeba Raja and Aidan Hughes and Yixuan Xu and Parham zarei and David G. Michelson and Stephan Sigg},
journal={IEEE Antennas and Wireless Propagation letters},
title={Wireless Multi-frequency Feature Set to Simplify Human 3D Pose Estimation},
year={2019},
volume={18},
number={5},
pages={876-880},
doi = {10.1109/LAWP.2019.2904580},
abstract = {We present a multifrequency feature set to detect driver's three-dimensional (3-D) head and torso movements from fluctuations in the radio frequency channel due to body movements. Current features used for movement detection are based on the time-of-flight, received signal strength, and channel state information and come with the limitations of coarse tracking, sensitivity toward multipath effects, and handling corrupted phase data, respectively. There is no standalone feature set that accurately detects small and large movements and determines the direction in 3-D 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 data from four participants reveal that the classification accuracy is 77.4% at 1.8 GHz, it is 87.4% at 30 GHz, and multifrequency feature set improves the accuracy to 92%.},
  project = {radiosense},
group = {ambience}}
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