Real-Time HR Estimation from wrist PPG using Binary LSTMs

Leandro Giacomini Rocha, Nick Van Helleputte, Muqing Liu, Dwaipayan Biswas, Bram Ernst Verhoef, Sergio Bampi, Chris H. Kim, Chris Van Hoof, Mario Konijnenburg, Marian Verhelst

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Wrist-worn photoplethysmography (PPG) sensors present a popular alternative to electrocardiogram recording for heart rate (HR) estimation. However, their accuracy is limited by motion artifacts inherent in ambulatory settings. In this paper, we propose a binarized neural network framework, b-CorNET, to efficiently estimate HR from single-channel wrist PPG signals during intense physical activity. The model comprises two binary convolution neural network layers followed by two binary long short-Term memory (b-LSTM) layers and a dense layer working on quantized PPG data. The proposed framework achieves an MAE of 3.75±3.05 bpm when evaluated on 12 IEEE SPC subjects. Furthermore, a novel, low-complexity architecture for the b-LSTM layers is proposed and efficiently mapped on a Xilinx Virtex5 FPGA, enabling HR computation.

Original languageEnglish (US)
Title of host publicationBioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509006175
StatePublished - Oct 2019
Event2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019 - Nara, Japan
Duration: Oct 17 2019Oct 19 2019

Publication series

NameBioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings


Conference2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019

Bibliographical note

Funding Information:
V. CONCLUSION In this paper a b-CorNET framework with a proof-of-concept b-LSTM implementation is presented to estimate HR from wrist PPG data. The results in comparison to nonbinary CorNET show a slight degradation (< 0.1bpm), due to binarization and quantization. Future work will further increase the robustness of the model and integrate the CNN layers along with LSTM for a complete PPG HR estimation system. ACKNOWLEDGMENT The first author would like to thank IFRS, CAPES and CNPq Brazilian agencies for supporting this research. REFERENCES

Publisher Copyright:
© 2019 IEEE.

Copyright 2020 Elsevier B.V., All rights reserved.


  • Binary LSTM
  • CNN
  • FPGA
  • PPG

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