Deep Learning-based Real-time Smartphone Pose Detection for Ultra-wideband Tagless Gate
<|reference_start|>Deep Learning-based Real-time Smartphone Pose Detection for Ultra-wideband Tagless Gate: As commercial interest in proximity services increased, the development of various wireless localization techniques was promoted. In line with this trend, Ultra-wideband (UWB) is emerging as a promising solution that can realize proximity services thanks to centimeter-level localization accuracy. In addition, since the actual location of the mobile device (MD) on the human body, called pose, affects the localization accuracy, poses are also important to provide accurate proximity services, especially for the UWB tagless gate (UTG). In this paper, a real-time pose detector, termed D3, is proposed to estimate the pose of MD when users pass through UTG. D3 is based on line-of-sight (LOS) and non-LOS (NLOS) classification using UWB channel impulse response and utilizes the inertial measurement unit embedded in the smartphone to estimate the pose. D3 is implemented on Samsung Galaxy Note20 Ultra (i.e., SMN986B) and Qorvo UWB board to show the feasibility and applicability. D3 achieved an LOS/NLOS classification accuracy of 0.984, and ultimately detected four different poses of MD with an accuracy of 0.961 in real-time.<|reference_end|>
@article{choi2024deep,
    title={Deep Learning-based Real-time Smartphone Pose Detection for
  Ultra-wideband Tagless Gate},
    author={Junyoung Choi and Sagnik Bhattacharya},
    journal={IEEE GLOBECOM 2023},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08399},
    primaryClass={eess.SP cs.NI}
}