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Binary CorNET: Accelerator for HR Estimation From Wrist-PPG

Journal Contribution - Journal Article Conference Contribution

Research on heart rate (HR) estimation using wrist-worn photoplethysmography (PPG) sensors have progressed rapidly owing to the prominence of commercial sensing modules, used widely for lifestyle monitoring. Reported methodologies have been fairly successful in mitigating the effect of motion artifacts (MA) in ambulatory environment for HR estimation. Recently, a learning framework, CorNET, employing two-layer convolution neural networks (CNN) and two-layer long short-term network (LSTM) was successfully reported for estimating HR from MA-induced PPG signals. However, such a network topology with large number of parameters presents a challenge, towards low-complexity hardware implementation aimed at on-node processing. In this paper, we demonstrate a fully binarized network (bCorNET) topology and its corresponding algorithm-to-architecture mapping and energy-efficient implementation for HR estimation. The proposed framework achieves a MAE of 6.67 ± 5.49 bpm when evaluated on 22 IEEE SPC subjects. The design, synthesized with ST65 nm technology library achieving 3 GOPS @ 1 MHz, consumes 56.1 μJ per window with occupied 1634K NAND2 equivalent cell area and had a latency of 32 ms when estimating HR every 2 s from PPG signals.
Journal: IEEE Transactions on Biomedical Circuits and Systems
ISSN: 1932-4545
Issue: 4
Volume: 14
Pages: 715 - 726
Number of pages: 12
Publication year:2020