The Case for tinyML in Healthcare: CNNs for Real-Time On-Edge Blood Pressure Estimation-Reference-Cited by-同舟云学术

The Case for tinyML in Healthcare: CNNs for Real-Time On-Edge Blood Pressure Estimation

Published:2023-03-27 Issue: Volume: Page:
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Container-title:Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
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Author:

Sun Bailian¹^ORCID,Bayes Safin¹^ORCID,Abotaleb Abdelrhman Mohamed¹^ORCID,Hassan Mohamed¹^ORCID

Affiliation:

1. McMaster University, Hamilton, ON, Canada

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3555776.3577747

Reference52 articles.

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2. Khaled Ahmed and Mohamed Hassan . 11-14 June 2022 . tinyCare: A tinyML-based Low-Cost Continuous Blood Pressure Estimation on the Extreme Edge . The 10th IEEE International Conference on Healthcare Informatics (11-14 June 2022). Khaled Ahmed and Mohamed Hassan. 11-14 June 2022. tinyCare: A tinyML-based Low-Cost Continuous Blood Pressure Estimation on the Extreme Edge. The 10th IEEE International Conference on Healthcare Informatics (11-14 June 2022).

3. Arnon Amir , Brian Taba , David Berg , Timothy Melano , Jeffrey McKinstry , Carmelo Di Nolfo , Tapan Nayak , Alexander Andreopoulos , Guillaume Garreau , Marcela Mendoza , 2017 . A low power, fully event-based gesture recognition system . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7243--7252 . Arnon Amir, Brian Taba, David Berg, Timothy Melano, Jeffrey McKinstry, Carmelo Di Nolfo, Tapan Nayak, Alexander Andreopoulos, Guillaume Garreau, Marcela Mendoza, et al. 2017. A low power, fully event-based gesture recognition system. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7243--7252.

4. An overview of deep learning in medical imaging

5. End-to-End Blood Pressure Prediction via Fully Convolutional Networks

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