Human Presence Estimation using RSSI
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- Evaluated articles (PREreview)
Abstract
This paper introduces a privacy-preserving system for detecting presence using Wi-Fi Received Signal Strength Indicator (RSSI) variations, capable of distinguishing humans from various objects. Multiple low-cost Wi-Fi nodes (arranged in a grid) and 2.4 GHz RF source capture signal disturbances, which are interpolated into heatmaps and combined with raw and filtered RSSI features for analysis via a Random Forest machine learning model. To address the challenge of labeling wireless data, cross-modal supervision integrates synchronized camera frames during training to guide the learning process, after which the system operates solely on RSSI inputs. The approach achieves 94% classification accuracy in evaluated scenarios, with potential to differentiate humans and objects through expanded node deployment. Unlike camera-dependent methods, it detects presence through obstacles without privacy concerns. Built with $50 hardware, it offers an affordable alternative for smart environment monitoring.
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This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/23312313.
Does the introduction explain the objective of the research presented in the preprint? Yes It contains the end goal of the research which was to make a method (low cost) for indoor sensingAre the methods well-suited for this research? Somewhat appropriate Better algorithms & hardware could be used but due to resource constrain these have been appliedAre the conclusions supported by the data? Somewhat supportedThis Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/23312313.
Does the introduction explain the objective of the research presented in the preprint? Yes It contains the end goal of the research which was to make a method (low cost) for indoor sensingAre the methods well-suited for this research? Somewhat appropriate Better algorithms & hardware could be used but due to resource constrain these have been appliedAre the conclusions supported by the data? Somewhat supportedAre the data presentations, including visualizations, well-suited to represent the data? Highly appropriate and clearHow clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Very clearlyIs the preprint likely to advance academic knowledge? Moderately likelyWould it benefit from language editing? NoWould you recommend this preprint to others? Yes, it's of high qualityIs it ready for attention from an editor, publisher or broader audience? Yes, as it isCompeting interests
The author declares that they have no competing interests.
Use of Artificial Intelligence (AI)
The author declares that they did not use generative AI to come up with new ideas for their review.
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