A novel data driven model of ageing postural control

Document identifier: oai:DiVA.org:ltu-76838
Keyword: Medical and Health Sciences, Elektroteknik och elektronik, Robotik och artificiell intelligens, Robotics and Artificial Intelligence, Fysioterapi, Reglerteknik, Control Engineering, Robotteknik och automation, Teknik och teknologier, Health Sciences, Robotics, Electrical Engineering, Electronic Engineering, Information Engineering, Engineering and Technology, Sjukgymnastik, Hälsovetenskaper, Medicin och hälsovetenskap, Physiotherapy, Automatic Control
Publication year: 2019
Relevant Sustainable Development Goals (SDGs):
SDG 3 Good health and wellbeingSDG 9 Industry, innovation and infrastructure
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

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.

Authors

Hedyeh Jafari

Luleå tekniska universitet; Signaler och system
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Mascha Pauelsen

Luleå tekniska universitet; Hälsa och rehabilitering
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Ulrik Röijezon

Luleå tekniska universitet; Hälsa och rehabilitering
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Lars Nyberg

Luleå tekniska universitet; Hälsa och rehabilitering
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George Nikolakopoulos

Luleå tekniska universitet; Signaler och system
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Thomas Gustafsson

Luleå tekniska universitet; Signaler och system
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