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  • 1.
    Jafari, Hedyeh
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On mimicking human balance with brain-inspired modeling and control2020Licentiate thesis, comprehensive summary (Other academic)
  • 2.
    Jafari, Hedyeh
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Castaño Arranz, Miguel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On Control Structure Design for a Walking Beam Furnace2017In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1355-1360, article id 7984307Conference paper (Refereed)
    Abstract [en]

    The aim of this article is to introduce a novel sparse controller design for the temperature control of an experimental walking beam furnace in steel industry. Adequate tracking of temperature references is essential for the quality of the heated slabs. However, the design of the temperature control is hindered by the multivariable (non-square) dynamic behavior of the furnace. These dynamics include significant loop interactions and time delays. Furthermore, a novel data-driven model, based on real life experimental data that relies on a subspace state representation in a closed loop approach is introduced. In the sequel, the derived model is utilized to investigate the controller's structure. By applying the relative gain array approach a decentralized feedback controller is designed. However, in spite of the optimal and sparse design of the controller, there exists interaction between loops. By analyzing the interaction between the inputs-outputs with the Σ2 Gramian-based interaction methodology, a decoupled multi-variable controller is implied. The simulation result, based on the experimental modeling of the furnace, shows that the controller can successfully decrease the interaction between the loops and track the reference temperature set-points.

  • 3.
    Jafari, Hedyeh
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Replicating human brain mechanisms towards balancing2019Conference paper (Refereed)
    Abstract [en]

    Understanding the performance of the human brain to stabilize the body remains an open fundamental research question. In this article, we study the hypothesis of internal model of the Central Nervous System (CNS) by a novel proposed architecture based on a recurrent neural network. The overall objective of the article and the main contribution stems from demonstrating the capability of replicating the balancing mechanisms of the brain by training the proposed bio-inspired network architecture with human balancing data and in the sequel applying the resulting control structure for controlling a single link inverted pendulum. Towards this direction, the body kinetics and kinematics measurements of forty-five subjects during upright stance trails were collected and utilized for training the proposed neural network. The efficacy of the proposed scheme will be proven through multiple simulation results with a single link inverted pendulum, where it will be demonstrated that the brain-inspired control scheme achieves a proper balance.

  • 4.
    Jafari, Hedyeh
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Pauelsen, Mascha
    Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.
    Röijezon, Ulrik
    Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.
    Nyberg, Lars
    Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A novel data driven model of ageing postural control2019Conference paper (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.

  • 5.
    Jafari, Hedyeh
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Pauelsen, Mascha
    Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.
    Röijezon, Ulrik
    Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.
    Nyberg, Lars
    Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On Internal Modeling of the Upright Postural Control in Elderly2018Conference paper (Refereed)
  • 6.
    Pauelsen, Mascha
    et al.
    Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.
    Jafari, Hedyeh
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Vikman, Irene
    Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.
    Nyberg, Lars
    Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.
    Röijezon, Ulrik
    Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.
    Using the frequency power spectrum to learn more about aging postural control and fall-related concerns2019Conference paper (Refereed)
    Abstract [en]

    Backgound:

    Posturography is widely used to describe and analyze human postural control. The traditional features of the center of pressure (CoP) trajectory during open eyes quiet standing tests have been used to show the association between declined sensorimotor systems and the variation in fall-related concerns (FrC), but seem to be too crude to separate each sensorimotor system’s contribution. Therefore, research has moved towards analysing the frequency domain of the CoP trajectory.

    Aim:

    To explore the frequency domain of CoP trajectory signals in an effort to learn more about ageing postural control and how it mediates and is mediated by FrC 

    Method:

    We recruited 45 people aged 70 or more. To measure body sway during quiet stance, we registered CoP trajectories using a force plate. A power spectral density analysis was performed on the CoP signal of all participants, from which we then extracted features: peak power, mean power, 50% power and 80% power. Principal component analysis, orthogonal projection to latent structures (OPLS), and OPLS-discriminant analysis were used to explore patterns of explanation of the features by a wide range of sensorimotor variables and FrC measured on the participants. A PLS-tree was used for the initial grouping.

    Results:

    The PLS-tree gave 2 groups. Group 2 had significantly more FrC, lower morale, larger errors in knee proprioception, slower reaction times, and weaker lower limb strength. They also had lower frequencies at all four features (significant at all but peak power).

    Conclusions:

    Under the assumption that the vision feedback loop generates more power in the lower frequencies of quiet stance sway, one explanation of the findings could be that once an individual starts experiencing postural control decline, vision gets weighed heavier in the integration process. More research is needed to find the most accurate ways to investigate postural control changes.

1 - 6 of 6
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