Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
A novel data driven model of ageing postural control
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
Luleå tekniska universitet, Institutionen för hälsovetenskap, Hälsa och rehabilitering.ORCID-id: 0000-0002-2510-7571
Luleå tekniska universitet, Institutionen för hälsovetenskap, Hälsa och rehabilitering.ORCID-id: 0000-0003-3901-0364
Luleå tekniska universitet, Institutionen för hälsovetenskap, Hälsa och rehabilitering.ORCID-id: 0000-0002-9813-2719
Vise andre og tillknytning
2019 (engelsk)Konferansepaper, Poster (with or without abstract) (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2019.
HSV kategori
Forskningsprogram
Fysioterapi; Robotik och artificiell intelligens
Identifikatorer
URN: urn:nbn:se:ltu:diva-76838OAI: oai:DiVA.org:ltu-76838DiVA, id: diva2:1372595
Konferanse
EU Falls Festival
Tilgjengelig fra: 2019-11-25 Laget: 2019-11-25 Sist oppdatert: 2019-11-25

Open Access i DiVA

Fulltekst mangler i DiVA

Søk i DiVA

Av forfatter/redaktør
Jafari, HedyehPauelsen, MaschaRöijezon, UlrikNyberg, LarsNikolakopoulos, GeorgeGustafsson, Thomas
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric

urn-nbn
Totalt: 40 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf