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
A novel data driven model of ageing postural control
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.ORCID iD: 0000-0002-2510-7571
Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.ORCID iD: 0000-0003-3901-0364
Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.ORCID iD: 0000-0002-9813-2719
Show others and affiliations
2019 (English)Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Background

Postural control is a complex system. Based on sensorimotor integration, the central nervous system (CNS) maintains balance by sending suitable motor commands to the muscles. Physiological decline due to ageing, affects balance performance through failing postural control – and in turn affects falls self-efficacy and activity participation. Understanding how the CNS adapts to these changes and predicts the appropriate motor commands to stabilize the body, has been a challenge for postural control research the latest years.

Aims

To understand and model the performance of the central nervous system as the controller of the human body.

Methods

Modelling was based on postural control data from 45 older adults (70 years and older). Ankle, knee and hip joint kinematics were measured during quiet stance using a motion capture system. Principal component analysis was used in order to reduce the measured multidimensional kinematics from a set of correlated discrete time series to a set of principal components. The outcome was utilized to predict the motor commands. The adaptive behaviour of the CNS was modelled by recurrent neural network including the efference copy for rapid predictions. The data from joint kinematics and electromyography (EMG) signals of the lower limb muscles were measured and separated into training and test data sets.

Results

The model can predict postural motor commands with very high accuracy regardless of a large physiological variability or balancing strategies. This model has three characteristics: a) presents an adaptive scheme to individual variability, 2) showcases the existence of an efference copy, and 3) is human experimental data driven.

Conclusion

The model can adapt to physical body characteristics and individual differences in balancing behaviour, while successfully predict motor commands. It should therefore be utilised in the continued pursuit of a better understanding of ageing postural control.

Place, publisher, year, edition, pages
2019.
National Category
Physiotherapy Robotics
Research subject
Physiotherapy; Robotics and Artificial Intelligence
Identifiers
URN: urn:nbn:se:ltu:diva-76838OAI: oai:DiVA.org:ltu-76838DiVA, id: diva2:1372595
Conference
EU Falls Festival
Available from: 2019-11-25 Created: 2019-11-25 Last updated: 2019-11-25

Open Access in DiVA

No full text in DiVA

Authority records BETA

Jafari, HedyehPauelsen, MaschaRöijezon, UlrikNyberg, LarsNikolakopoulos, GeorgeGustafsson, Thomas

Search in DiVA

By author/editor
Jafari, HedyehPauelsen, MaschaRöijezon, UlrikNyberg, LarsNikolakopoulos, GeorgeGustafsson, Thomas
By organisation
Signals and SystemsHealth and Rehabilitation
PhysiotherapyRobotics

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 70 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