Novel Low Memory Footprint DNN Models for Edge Classification of Surgeons’ Postures-Reference-Cited by-同舟云学术

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Novel Low Memory Footprint DNN Models for Edge Classification of Surgeons’ Postures

Published:2023-03 Issue:1 Volume:15 Page:21-24
ISSN:1943-0663
Container-title:IEEE Embedded Systems Letters
language:
Short-container-title:IEEE Embedded Syst. Lett.

Author:

Hanneman Alex¹^ORCID,Fawden Terry²^ORCID,Branciforte Marco³^ORCID,Virzì Maria Celvisia³,Moss Esther L.⁴^ORCID,Ost Luciano¹^ORCID,Zecca Massimiliano¹^ORCID

Affiliation:

1. Wolfson School, Loughborough University, Loughborough, U.K.

2. Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, U.K.

3. Industrial and Power Group (IPG) System Lab, STMicroelectronics s.r.l., Catania, Italy

4. Leicester Cancer Research Centre, University of Leicester, Leicester, U.K.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Computer Science,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/4563995/10054570/09828506.pdf?arnumber=9828506

Reference15 articles.

1. A Resource Constrained Neural Network for the Design of Embedded Human Posture Recognition Systems

2. Evaluating surgical skills from kinematic data using convolutional neural networks;fawaz;Medical Image Computing and Computer Assisted Intervention,2018

3. A public domain dataset for human activity recognition using smartphones;anguita;Proc 21st ESANN,2013

4. Objective assessment of surgeon kinematics during simulated laparoscopic surgery: a preliminary evaluation of the effect of high body mass index models

5. Deep learning with convolutional neural network for objective skill evaluation in robot-assisted surgery

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