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Transfer learning autoencoder used for compressing multimodal biosignal

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Abstract

Electrocardiogram, electromyogram, electroencephalogram are the foremost required vital signs for diagnosing chronic diseases like sleep disorder, mood disorder, epilepsy etc., which demands long-term monitoring. A sensor based wearable system which is enabled with internet technology, supports the continuous recordings of these vital signs without troubling the patient’s daily activities. And the wearable hub is responsible for collecting the readings of biosignals from multiple micro-sensor nodes deployed around the body which creates the short range of communication and forward to the observer. These continuous monitoring increases the signal transmission cost and declines the battery life of wearables. So, the observed multiple biosignals can be compressed jointly than individually before sending, at an edge level. This paper proposes transfer learning based multimodal convolutional denoising auto encoder to perform multimodal compression and to reconstruct the data from its latent representation. Transfer learning helps the system to reuse the learned weights which may reconstruct the data with better quality score than by randomly initialized weights. The proposed work achieves compression ratio of 128 and it is proved that multimodal compression is better than unimodal compression in case of consuming multiple sensors. And the experimental result proves that the computation cost is low in multimodal compression than in unimodal compression.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation (KLEF),, Green Fields, Vaddeswaram, Guntur District, Vijayawada, Andhra Pradesh, India

    Ithaya Rani Panneerselvam

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Panneerselvam, I.R. Transfer learning autoencoder used for compressing multimodal biosignal. Multimed Tools Appl 81, 17547–17565 (2022). https://doi.org/10.1007/s11042-022-12597-6

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