Datasets
Standard Dataset
BodySensor

- Citation Author(s):
- Submitted by:
- Siyuan Chen
- Last updated:
- Mon, 04/21/2025 - 04:05
- DOI:
- 10.21227/dg6x-vz07
- License:
- Categories:
- Keywords:
Abstract
Real-time tracking of electricians in distribution rooms is essential for ensuring operational safety. Traditional GPS-based methods, however, are ineffective in such environments due to complex non-line-of-sight (NLOS) conditions caused by dense cabinets and thick walls that obstruct satellite signals. Existing solutions, such as video-based systems, are prone to inaccuracies due to NLOS effects, while wearable devices often prove inconvenient for workers. To address these challenges, we propose BodySensor, a contactless system that utilizes a single commercial off-the-shelf (COTS) ultra-wideband (UWB) radar for real-time, fine-grained indoor tracking in distribution rooms. Our approach incorporates the Native Resolution Vision Transformer (NaViT), a Vision Transformer variant, to identify human positions by analyzing nonlinear multipath signals. Furthermore, we introduce an RF-to-real position mapping to enhance positioning accuracy within detected intervals. Extensive experiments conducted in three distinct environments, encompassing over 30 hours of data, demonstrate that BodySensor achieves a coarse localization accuracy of up to $99.8\%$, with a mean tracking error of approximately 6.2 cm under line-of-sight (LOS) conditions and 10.9 cm under full NLOS conditions. The system maintains consistent accuracy regardless of whether the subject is stationary or in motion, underscoring its robustness and practicality. BodySensor exemplifies the potential of radar-based solutions for smart power systems, providing a reliable and non-intrusive alternative for enhancing worker safety and operational efficiency.
In this dataset, radar data of human body in both stationary and moving states are included. They are organized into three major categories of datasets: the human motion dataset, the human stationary dataset, and the comprehensive dataset. For example, the comprehensive dataset consists of five categories: human presence in intervals 1, 2, and 3, human absence from the power distribution room, and human presence in the power distribution room but not within the intervals.
Dataset Files
- data_all.zip (10.84 GB)
- 25_01_07_dingwei_camera.zip (64.32 MB)