Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Data-driven modelling of fatigue in pelvic floor muscles when performing Kegel exercises
Luleå University of Technology. Univ Auckland, Auckland Bioengn Inst, Auckland, New Zealand.
Univ Auckland, Auckland Bioengn Inst, Auckland, New Zealand.
Univ Auckland, Auckland Bioengn Inst, Auckland, New Zealand.
Univ Auckland, Auckland Bioengn Inst, Auckland, New Zealand. Univ Auckland, Dept Engn Sci, Auckland, New Zealand.
Show others and affiliations
2020 (English)In: 2019 IEEE 58th Conference on Decision and Control (CDC), IEEE, 2020, p. 5647-5653Conference paper, Published paper (Refereed)
Abstract [en]

This paper studies how to describe, using a piecewise linear dynamical model, the short-term effects of fatigue and recovery on the strength of pelvic floor muscles. Specifically, we first adapt a known model that describes short-term fatigue in skeletal muscles to the specific problem of describing fatigue in pelvic floor muscles when performing Kegel exercises, and then propose a strategy to learn the modelřs parameters from field data. In details, we estimate the model parameters using a least squares approach starting from measurement data that has been obtained from three healthy women using a dedicated vaginal pressure sensor array and a connected mobile app which gamifies the Kegel exercising experience. We show that describing the pelvic floor muscles behaviour in terms of short-term fatigue and recovery factors plus learning the associated parameters from data from healthy women leads to the possibility of precisely forecasting how much pressure the players will exert while playing the game. By cross-learning and cross-testing individual models from the three volunteers we also discover that the models need to be individualized: indeed, the numerical results indicate that, generically, using data from one player to model another leads to potentially drastically lower forecasting capabilities.

Place, publisher, year, edition, pages
IEEE, 2020. p. 5647-5653
Series
IEEE Conference on Decision and Control, ISSN 0743-1546, E-ISSN 2576-2370
Keywords [en]
pelvic floor muscles, short-term fatigue, skeletal muscles, least squares, vaginal pressure sensor array, mobile app, Kegel exercises
National Category
Obstetrics, Gynecology and Reproductive Medicine
Identifiers
URN: urn:nbn:se:ltu:diva-81698DOI: 10.1109/CDC40024.2019.9029629ISI: 000560779005035Scopus ID: 2-s2.0-85082492571OAI: oai:DiVA.org:ltu-81698DiVA, id: diva2:1504634
Conference
58th IEEE Conference on Decision and Control (CDC 2019), 11-13 December, 2019, Nice, France
Note

ISBN för värdpublikation: 978-1-7281-1398-2

Available from: 2020-11-30 Created: 2020-11-30 Last updated: 2024-08-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Varagnolo, Damiano

Search in DiVA

By author/editor
Kask, NathalieVaragnolo, Damiano
By organisation
Luleå University of Technology
Obstetrics, Gynecology and Reproductive Medicine

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 162 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf