Data-driven path loss estimation in human body communication: enhancing efficiency via parameter prioritization and transfer learning

This work proposes an efficient machine learning approach for path loss estimation in human body communication, focusing on in-body (IB) and on-body (OB) scenarios. Friis' formula and surface wave-based model often have limitations, especially considering variations in electrical properties (EPs), antenna placement, and propagation effects. Here, artificial neural networks (ANNs) are used with parameter prioritization and transfer learning to enhance efficiency. An IB-to-OB link (10 MHz-50 MHz) is analyzed, demonstrating that ANN achieves about 80% accuracy with limited data. OB-to-IB communication at 13.56 MHz, 2.45 GHz, and 5.8 GHz is investigated, incorporating EP and antenna orientation variations. Results show ANN with prioritization reduces input parameters by 70%, maintaining up to 95% accuracy. Additionally, transfer learning enables path loss prediction for 5.8 GHz scenario using a pre-trained 2.45 GHz dataset, achieving over 93% accuracy, significantly reducing computational needs.

Subjects

Wireless body area networks

Human body communication path loss

artificial neural networks

Parameter prioritization

Transfer learning

DDC Class

600: Technology

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Data-driven path loss estimation in human body communication: enhancing efficiency via parameter prioritization and transfer learning