Neural network-based Bluetooth synchronization of multiple wearable devices

Karthikeyan Kalyanasundaram Balasubramanian (), Andrea Merello, Giorgio Zini, Nathan Charles Foster, Andrea Cavallo, Cristina Becchio and Marco Crepaldi ()
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Karthikeyan Kalyanasundaram Balasubramanian: Istituto Italiano di Tecnologia
Andrea Merello: Istituto Italiano di Tecnologia
Giorgio Zini: Istituto Italiano di Tecnologia
Nathan Charles Foster: Istituto Italiano di Tecnologia
Andrea Cavallo: Istituto Italiano di Tecnologia
Cristina Becchio: Istituto Italiano di Tecnologia
Marco Crepaldi: Istituto Italiano di Tecnologia

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract Bluetooth-enabled wearables can be linked to form synchronized networks to provide insightful and representative data that is exceptionally beneficial in healthcare applications. However, synchronization can be affected by inevitable variations in the component’s performance from their ideal behavior. Here, we report an application-level solution that embeds a Neural network to analyze and overcome these variations. The neural network examines the timing at each wearable node, recognizes time shifts, and fine-tunes a virtual clock to make them operate in unison and thus achieve synchronization. We demonstrate the integration of multiple Kinematics Detectors to provide synchronized motion capture at a high frequency (200 Hz) that could be used for performing spatial and temporal interpolation in movement assessments. The technique presented in this work is general and independent from the physical layer used, and it can be potentially applied to any wireless communication protocol.

Date: 2023
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DOI: 10.1038/s41467-023-40114-2

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