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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 re- searchers 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 setup 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 × 6 sq m and 4.5 × 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,only1% 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.
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Poster Abstract: Scaling Device-free Indoor Tracking based on Self Calibration
The democratization of indoor tracking systems lays the ground- work for a wide spectrum of smart home applications. While prior work on RF-based device-free localization/tracking have shown preferable features and promising results, they heavily relied on well-calibrated sensor placements, which require hours of intensive manual setup and respective expertise, making it prohibitively expensive to scale deployments to wide range (e.g., tens or hundreds of real homes). We propose SCALING, a plug-and-play indoor tracking system, of which the key enabler is a self calibrating algorithm that estimates the distributed sensor locations through their distance measurements to a person walking a trajectory, a trivial effort without taxing layman users physically or cognitively. We have experimentally evaluated SCALING via real world testbeds and shown an 80-percentile tracking accuracy of 40.5 cm, only 1% degradation compared to the classical multilateration with known sensor locations (anchors), which costs hours of intensive calibrating effort.
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- Award ID(s):
- 1951880
- PAR ID:
- 10439724
- Date Published:
- Journal Name:
- ACM Conference on Embedded Networked Sensor Systems
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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 presentSCALING, 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 evaluateSCALINGvia 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 thatSCALINGoutperformed 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 withSCALINGcompared 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.more » « less
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Accepted Manuscript1.0
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