Quantifying balance control during stance: a multivariate system identification approach

Denise Engelhart

Research output: ThesisPhD Thesis - Research UT, graduation UT

103 Downloads (Pure)

Abstract

Balance control involves the contribution of neural, muscular and sensory systems, which work together via complex feedback pathways in a closed loop. With age or disease, the underlying systems in balance control can deteriorate; e.g. muscle strength decreases, the sensory systems become less accurate, the processing time of sensory information increases and the neural conduction time increases. To maintain balance in various situations and to prevent falling, the underlying systems can compensate for each other’s deterioration; i.e. there exists some redundancy within the closed loop system of balance control. However, when the deterioration in the underlying system is too severe, or when the compensation mechanism is also affected, impaired balance tends to become symptomatic and the risk of falling increases. To prescribe targeted therapy on an individual level to reduce the consequences of falls, it is important to detect the primarily deteriorated underlying system and the compensation strategies that are at work. Available clinical balance tests have little influence on clinical decision making. In this thesis, a novel experimental set-up and data-analysis method was introduced to assess the contribution of the underlying mechanisms in standing balance control; a multivariate system identification approach. With this experimental approach we quantified age-related changes in standing balance control and we validated the applicability for in clinical practice. The application of system identification techniques give more insight in the underlying physiology of balance control and the changes with age. Using the techniques as a diagnostic tool, can help to detect impaired balance and ultimately reduce the consequences of falls in the elderly.
Original languageEnglish
Awarding Institution
  • University of Twente
Supervisors/Advisors
  • van der Kooij, Herman , Supervisor
  • Schouten, Alfred Christiaan, Advisor
  • Aarts, Ronald, Advisor
Award date3 Sep 2015
Place of PublicationEnschede
Publisher
Print ISBNs978-90-365-3922-7
DOIs
Publication statusPublished - 3 Sep 2015

Fingerprint

Accidental Falls
Neural Conduction
Muscle Strength
Therapeutics

Keywords

  • METIS-311248
  • IR-96845

Cite this

Engelhart, Denise. / Quantifying balance control during stance : a multivariate system identification approach. Enschede : University of Twente, 2015. 160 p.
@phdthesis{957678651def4cc592073b80fd06c580,
title = "Quantifying balance control during stance: a multivariate system identification approach",
abstract = "Balance control involves the contribution of neural, muscular and sensory systems, which work together via complex feedback pathways in a closed loop. With age or disease, the underlying systems in balance control can deteriorate; e.g. muscle strength decreases, the sensory systems become less accurate, the processing time of sensory information increases and the neural conduction time increases. To maintain balance in various situations and to prevent falling, the underlying systems can compensate for each other’s deterioration; i.e. there exists some redundancy within the closed loop system of balance control. However, when the deterioration in the underlying system is too severe, or when the compensation mechanism is also affected, impaired balance tends to become symptomatic and the risk of falling increases. To prescribe targeted therapy on an individual level to reduce the consequences of falls, it is important to detect the primarily deteriorated underlying system and the compensation strategies that are at work. Available clinical balance tests have little influence on clinical decision making. In this thesis, a novel experimental set-up and data-analysis method was introduced to assess the contribution of the underlying mechanisms in standing balance control; a multivariate system identification approach. With this experimental approach we quantified age-related changes in standing balance control and we validated the applicability for in clinical practice. The application of system identification techniques give more insight in the underlying physiology of balance control and the changes with age. Using the techniques as a diagnostic tool, can help to detect impaired balance and ultimately reduce the consequences of falls in the elderly.",
keywords = "METIS-311248, IR-96845",
author = "Denise Engelhart",
year = "2015",
month = "9",
day = "3",
doi = "10.3990/1.9789036539227",
language = "English",
isbn = "978-90-365-3922-7",
publisher = "University of Twente",
address = "Netherlands",
school = "University of Twente",

}

Quantifying balance control during stance : a multivariate system identification approach. / Engelhart, Denise.

Enschede : University of Twente, 2015. 160 p.

Research output: ThesisPhD Thesis - Research UT, graduation UT

TY - THES

T1 - Quantifying balance control during stance

T2 - a multivariate system identification approach

AU - Engelhart, Denise

PY - 2015/9/3

Y1 - 2015/9/3

N2 - Balance control involves the contribution of neural, muscular and sensory systems, which work together via complex feedback pathways in a closed loop. With age or disease, the underlying systems in balance control can deteriorate; e.g. muscle strength decreases, the sensory systems become less accurate, the processing time of sensory information increases and the neural conduction time increases. To maintain balance in various situations and to prevent falling, the underlying systems can compensate for each other’s deterioration; i.e. there exists some redundancy within the closed loop system of balance control. However, when the deterioration in the underlying system is too severe, or when the compensation mechanism is also affected, impaired balance tends to become symptomatic and the risk of falling increases. To prescribe targeted therapy on an individual level to reduce the consequences of falls, it is important to detect the primarily deteriorated underlying system and the compensation strategies that are at work. Available clinical balance tests have little influence on clinical decision making. In this thesis, a novel experimental set-up and data-analysis method was introduced to assess the contribution of the underlying mechanisms in standing balance control; a multivariate system identification approach. With this experimental approach we quantified age-related changes in standing balance control and we validated the applicability for in clinical practice. The application of system identification techniques give more insight in the underlying physiology of balance control and the changes with age. Using the techniques as a diagnostic tool, can help to detect impaired balance and ultimately reduce the consequences of falls in the elderly.

AB - Balance control involves the contribution of neural, muscular and sensory systems, which work together via complex feedback pathways in a closed loop. With age or disease, the underlying systems in balance control can deteriorate; e.g. muscle strength decreases, the sensory systems become less accurate, the processing time of sensory information increases and the neural conduction time increases. To maintain balance in various situations and to prevent falling, the underlying systems can compensate for each other’s deterioration; i.e. there exists some redundancy within the closed loop system of balance control. However, when the deterioration in the underlying system is too severe, or when the compensation mechanism is also affected, impaired balance tends to become symptomatic and the risk of falling increases. To prescribe targeted therapy on an individual level to reduce the consequences of falls, it is important to detect the primarily deteriorated underlying system and the compensation strategies that are at work. Available clinical balance tests have little influence on clinical decision making. In this thesis, a novel experimental set-up and data-analysis method was introduced to assess the contribution of the underlying mechanisms in standing balance control; a multivariate system identification approach. With this experimental approach we quantified age-related changes in standing balance control and we validated the applicability for in clinical practice. The application of system identification techniques give more insight in the underlying physiology of balance control and the changes with age. Using the techniques as a diagnostic tool, can help to detect impaired balance and ultimately reduce the consequences of falls in the elderly.

KW - METIS-311248

KW - IR-96845

U2 - 10.3990/1.9789036539227

DO - 10.3990/1.9789036539227

M3 - PhD Thesis - Research UT, graduation UT

SN - 978-90-365-3922-7

PB - University of Twente

CY - Enschede

ER -