PowerSkel: A Device-Free Framework Using CSI Signal for Human Skeleton Estimation in Power Station
IEEE Internet of Things Journal(2024)
摘要
Safety monitoring of power operations in power stations is crucial for
preventing accidents and ensuring stable power supply. However, conventional
methods such as wearable devices and video surveillance have limitations such
as high cost, dependence on light, and visual blind spots. WiFi-based human
pose estimation is a suitable method for monitoring power operations due to its
low cost, device-free, and robustness to various illumination conditions.In
this paper, a novel Channel State Information (CSI)-based pose estimation
framework, namely PowerSkel, is developed to address these challenges.
PowerSkel utilizes self-developed CSI sensors to form a mutual sensing network
and constructs a CSI acquisition scheme specialized for power scenarios. It
significantly reduces the deployment cost and complexity compared to the
existing solutions. To reduce interference with CSI in the electricity
scenario, a sparse adaptive filtering algorithm is designed to preprocess the
CSI. CKDformer, a knowledge distillation network based on collaborative
learning and self-attention, is proposed to extract the features from CSI and
establish the mapping relationship between CSI and keypoints. The experiments
are conducted in a real-world power station, and the results show that the
PowerSkel achieves high performance with a PCK@50 of 96.27
significant visualization on pose estimation, even in dark environments. Our
work provides a novel low-cost and high-precision pose estimation solution for
power operation.
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关键词
Electric power operation safety,human pose estimation,channel state information,WiFi sensing,deep learning
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