Modelling the indentation force response of non-uniform soft tissue using a recurrent neural network

Rohan Nowell, Bijan Shirinzadeh, Julian Smith, Yongmin Zhong

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)

Abstract

A scaled recurrent neural network (RNN) model is developed which accurately predicts the force response from the indentation of a non-uniform soft tissue sample. The model consists of two components. The RNN is used to predict the force response of indentation using data from a reference tissue sample. A two-parameter component then scales the neural networks predictions relative to previously determined properties of the test sample. This component is based on a strain inverse model of force, which is used to account for the non-uniformity of the tissue between the test and reference data. Experimental force measurements were performed on a highly non-uniform soft tissue analogue to develop and validate the model. Using the visco-elastic Hunt-Crossley model as a benchmark, the developed model provides significantly better prediction. Future research will investigate applying this model to surgical simulations and verifying its application to different biological tissues.

Original languageEnglish
Title of host publication2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob 2016)
EditorsA. Bezerianos, H.I. Krebs, S.L. Kukreja
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages377-382
Number of pages6
ISBN (Electronic)9781509032877
ISBN (Print)9781509032884
DOIs
Publication statusPublished - 26 Jul 2016
EventInternational Conference on Biomedical Robotics and Biomechatronics 2016 - National University of Singapore, University Town, Singapore
Duration: 26 Jun 201629 Jun 2016
Conference number: 6th

Conference

ConferenceInternational Conference on Biomedical Robotics and Biomechatronics 2016
Abbreviated titleBioRob 2016
CountrySingapore
CityUniversity Town
Period26/06/1629/06/16

Cite this

Nowell, R., Shirinzadeh, B., Smith, J., & Zhong, Y. (2016). Modelling the indentation force response of non-uniform soft tissue using a recurrent neural network. In A. Bezerianos, H. I. Krebs, & S. L. Kukreja (Eds.), 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob 2016) (pp. 377-382). [7523655] IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BIOROB.2016.7523655