A TinyML-approach to detect the proximity of people based on bluetooth low energy beacons

Girolami M., Fattori F., Chessa S.

Proximity TinyML  Deep Learning  Arduino 

Proximity detection is the process of estimating the closeness between a target and a point of interest, and it can be estimated with different technologies and techniques. In this paper we focus on how detecting proximity between people with a TinyML-based approach. We analyze RSS values (Received Signal Strength) estimated by a micro-controller and propagated by Bluetooth's tags. To this purpose, we collect a dataset of Bluetooth RSS signals by considering different postures of the involved people. The dataset is adopted to train and test two neural networks: a fully-connected and an LSTM model that we compress to be executed directly on-board of the micro-controller. Experimental results conducted over the dataset show an average precision and recall metrics of 0.8 with both of the models, and with an inference time less than 1 ms.

Source: IE 2023 - 19th International Conference on Intelligent Environments, Island of Mauritius, 29-30/06/2023

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