AI Indoor Localization | O. Bin Tariq | PitchD 14
Channel: IEEE PoliTO Student Branch
Date: 16/02/2020

PitchD – the PhD’s pitch: our PhD IEEE Student Members explain to students, collegues and professors their research.

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Tag-Less Indoor Localization with AI and Capacitive Sensors

Mr. Osama Bin Tariq, Dept. of Electronics (DET), Politecnico di Torino

Low cost, ubiquitous, tag-less, and privacy-aware indoor monitoring is essential to many applications, such as home automation and assisted living of elderly persons. Capacitive sensors can be inconspicuous, cost and power-effective solution, but afflicted by noise especially at ranges much longer than their plate diameter. Here, we explore how well different Artificial Intelligence (AI) techniques can extract location and movement information from noisy experimental data (with both high-pitch and slow drift noise) obtained from capacitive sensors operating in load mode at long ranges. For a 3 m x 3 m space equipped with four 16 cm x 16 cm capacitive sensors, we investigate first how well Machine Learning (ML) can localize a person in 16 predefined positions, then how well continuous human walking trajectories can be reconstructed by various types of Neural Network architectures. We discuss how the experimental data was obtained, preprocessed, and the outcome of the various ML and AI techniques. The key result is the ability to locate and track a person in the above setting with an average accuracy of 93% for location classification and a mean error of 28 cm for trajectory reconstruction using sensors with an estimated SNR of about 3-4 dB.