SCALING: plug-n-play device-free indoor tracking
Abstract 24/7 continuous recording of in-home daily trajectories is informative for health status assessment (e.g., monitoring Alzheimer’s, dementia based on behavior patterns). Indoor device-free localization/tracking are ideal because no user efforts on wearing devices are needed. However, prior w...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-53524-z |
| _version_ | 1797274944537624576 |
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| author | Zongxing Xie Fan Ye |
| author_facet | Zongxing Xie Fan Ye |
| author_sort | Zongxing Xie |
| collection | DOAJ |
| description | Abstract 24/7 continuous recording of in-home daily trajectories is informative for health status assessment (e.g., monitoring Alzheimer’s, dementia based on behavior patterns). Indoor device-free localization/tracking are ideal because no user efforts on wearing devices are needed. However, prior work mainly focused on improving the localization accuracy. They relied on well-calibrated sensor placements, which require hours of intensive manual setup and respective expertise, feasible only at small scale and by mostly researchers themselves. Scaling the deployments to tens or hundreds of real homes, however, would incur prohibitive manual efforts, and become infeasible for layman users. We present SCALING, a plug-and-play indoor trajectory monitoring system that layman users can easily set up by walking a one-minute loop trajectory after placing radar nodes on walls. It uses a self calibrating algorithm that estimates sensor locations through their distance measurements to the person walking the trajectory, a trivial effort without taxing layman users physically or cognitively. We evaluate SCALING via simulations and two testbeds (in lab and home configurations of sizes 3 $$\times$$ × 6 sq m and 4.5 $$\times$$ × 8.5 sq m). Experimental results demonstrate that SCALING outperformed the baseline using the approximate multidimensional scaling (MDS, the most relevant method in the context of self calibration) by 3.5 m/1.6 m in 80-percentile error of self calibration and tracking, respectively. Notably, only 1% degradation in performance has been observed with SCALING compared to the classical multilateration with known sensor locations (anchors), which costs hours of intensive calibrating effort. In addition, we conduct Monte Carlo experiments to numerically analyze the impact of sensor placements and develop practical guidelines for deployment in real life scenarios. |
| first_indexed | 2024-03-07T15:06:20Z |
| format | Article |
| id | doaj.art-977eb49459bb4fd697a6b6876cd4f2fa |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:06:20Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-977eb49459bb4fd697a6b6876cd4f2fa2024-03-05T18:52:43ZengNature PortfolioScientific Reports2045-23222024-02-0114112310.1038/s41598-024-53524-zSCALING: plug-n-play device-free indoor trackingZongxing Xie0Fan Ye1Electrical and Computer Engineering Department, Stony Brook UniversityElectrical and Computer Engineering Department, Stony Brook UniversityAbstract 24/7 continuous recording of in-home daily trajectories is informative for health status assessment (e.g., monitoring Alzheimer’s, dementia based on behavior patterns). Indoor device-free localization/tracking are ideal because no user efforts on wearing devices are needed. However, prior work mainly focused on improving the localization accuracy. They relied on well-calibrated sensor placements, which require hours of intensive manual setup and respective expertise, feasible only at small scale and by mostly researchers themselves. Scaling the deployments to tens or hundreds of real homes, however, would incur prohibitive manual efforts, and become infeasible for layman users. We present SCALING, a plug-and-play indoor trajectory monitoring system that layman users can easily set up by walking a one-minute loop trajectory after placing radar nodes on walls. It uses a self calibrating algorithm that estimates sensor locations through their distance measurements to the person walking the trajectory, a trivial effort without taxing layman users physically or cognitively. We evaluate SCALING via simulations and two testbeds (in lab and home configurations of sizes 3 $$\times$$ × 6 sq m and 4.5 $$\times$$ × 8.5 sq m). Experimental results demonstrate that SCALING outperformed the baseline using the approximate multidimensional scaling (MDS, the most relevant method in the context of self calibration) by 3.5 m/1.6 m in 80-percentile error of self calibration and tracking, respectively. Notably, only 1% degradation in performance has been observed with SCALING compared to the classical multilateration with known sensor locations (anchors), which costs hours of intensive calibrating effort. In addition, we conduct Monte Carlo experiments to numerically analyze the impact of sensor placements and develop practical guidelines for deployment in real life scenarios.https://doi.org/10.1038/s41598-024-53524-z |
| spellingShingle | Zongxing Xie Fan Ye SCALING: plug-n-play device-free indoor tracking Scientific Reports |
| title | SCALING: plug-n-play device-free indoor tracking |
| title_full | SCALING: plug-n-play device-free indoor tracking |
| title_fullStr | SCALING: plug-n-play device-free indoor tracking |
| title_full_unstemmed | SCALING: plug-n-play device-free indoor tracking |
| title_short | SCALING: plug-n-play device-free indoor tracking |
| title_sort | scaling plug n play device free indoor tracking |
| url | https://doi.org/10.1038/s41598-024-53524-z |
| work_keys_str_mv | AT zongxingxie scalingplugnplaydevicefreeindoortracking AT fanye scalingplugnplaydevicefreeindoortracking |