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

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journal contribution
posted on 02.08.2022, 15:59 authored by Alex Hanneman, Terry Fawden, Marco Branciforte, Maria Celvisia Virzi, Esther L Moss, Luciano Ost, Massimiliano Zecca

Skill assessment is fundamental to enhance current laparoscopic surgical training and reduce the incidence of musculoskeletal injuries from performing these procedures. Recently, deep neural networks (DNNs) have been used to improve human posture and surgeons’ skills training. While they work well in lab, they normally require significant computational power which makes it impossible to use them on edge devices. This paper presents two low memory footprint DNN models used for classifying laparoscopic surgical skill levels at the edge. Trained models were deployed on three Arm Cortex-M processors using the X-Cube-AI and TensorFlow Lite Micro (TFLM) libraries. Results show that the CUBE-AI-based models give the best relative performance, memory footprint, and accuracy trade-offs when executed on the Cortex-M7.

History

Author affiliation

Leicester Cancer Research Centre, University of Leicester

Version

AM (Accepted Manuscript)

Published in

IEEE Embedded Systems Letters

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

issn

1943-0663

eissn

1943-0671

Copyright date

2022

Available date

02/08/2022

Language

en