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In 1993 Merfeld et al. [77], using a modelling approach in accordance with Luenberger’s the- ory [67], proposed a non-linear one-dimensional model of the human perception of spatial orienta- tion. The construction of the Merfeld’s model [77] was based on the concept illustrated in Figure 1. According to this concept, the model of spatial ori- entation perception is divided into two parts: the model of the physical world and the CNS model, that is represented by so-called internal model. The model of the physical world describes the dynamics of physical sensors (three semicircular canals and two otolith organs), while the internal model represents the sensory signals processing in the CNS.

Figure 1 In 1993 Merfeld et al. [77], using a modelling approach in accordance with Luenberger’s the- ory [67], proposed a non-linear one-dimensional model of the human perception of spatial orienta- tion. The construction of the Merfeld’s model [77] was based on the concept illustrated in Figure 1. According to this concept, the model of spatial ori- entation perception is divided into two parts: the model of the physical world and the CNS model, that is represented by so-called internal model. The model of the physical world describes the dynamics of physical sensors (three semicircular canals and two otolith organs), while the internal model represents the sensory signals processing in the CNS.

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nals in CNS. This model (fig. 3) has been validated on the basis of the results of experimental studies using monkeys [76,77]. By empirical regulation of four internal weighted parameters (k, j Kas Kok this model has the ability to predict the response to a wide range of motion stimuli, including VAR and OVAR. It is known, that every linear accelerometer is not able to distinguish the direction of the grav- itational vector from linear acceleration vector. This problem also applies to the otolith organ and is commonly referred to as gravito-inertial acceleration (GIA). The above mentioned prob- lem of sensory data interpretation occurs when
nals in CNS. This model (fig. 3) has been validated on the basis of the results of experimental studies using monkeys [76,77]. By empirical regulation of four internal weighted parameters (k, j Kas Kok this model has the ability to predict the response to a wide range of motion stimuli, including VAR and OVAR. It is known, that every linear accelerometer is not able to distinguish the direction of the grav- itational vector from linear acceleration vector. This problem also applies to the otolith organ and is commonly referred to as gravito-inertial acceleration (GIA). The above mentioned prob- lem of sensory data interpretation occurs when
In the work on modelling the human spatial ori- entation, it is worth mentioning a study of Laurens and Droulez’s [63], who have constructed a mode based on the method of Bayesian processing. This processing is carried out according to the proba- bilistic Bayesian estimation algorithm, which re- currently updates the previous system state with Fig.4. | The extended spatial orientation model based on the vestibular system [84].
In the work on modelling the human spatial ori- entation, it is worth mentioning a study of Laurens and Droulez’s [63], who have constructed a mode based on the method of Bayesian processing. This processing is carried out according to the proba- bilistic Bayesian estimation algorithm, which re- currently updates the previous system state with Fig.4. | The extended spatial orientation model based on the vestibular system [84].
Tab. 1. Validation studies and weighting parameters of state observer and KF/EKF models [87,116] the appearance of new observations. This three- dimensional vestibular model was successfully tested in numerical computational experiments. As a result, the model proved to be effective in es- timating VOR.
Tab. 1. Validation studies and weighting parameters of state observer and KF/EKF models [87,116] the appearance of new observations. This three- dimensional vestibular model was successfully tested in numerical computational experiments. As a result, the model proved to be effective in es- timating VOR.
from head-fixed to limbic coordinate system. Each of the above signals are processed by the suitable model of the visual sensor VIS, (where: i - x, X, W,, g,). The response a, from model VIS, is compared with the signal a, (designation with “4”) estimated by the internal model of this sensor. The result of
from head-fixed to limbic coordinate system. Each of the above signals are processed by the suitable model of the visual sensor VIS, (where: i - x, X, W,, g,). The response a, from model VIS, is compared with the signal a, (designation with “4”) estimated by the internal model of this sensor. The result of
g.6. Timeline of development of velocity storage, observer and optimal control perception models [87]. Over the past several decades, mathematical models of the human perception of spatial ori- entation based on the concept derived from the estimation theory have been widely used in ves- tibular system studies. Most of these models use one of the three estimation techniques: Bayesian estimation [60,63,68], Kalman’s filter (KF)[11,13,103] with its variations [9,116] and the state observer In the models of spatial orientation perception a some simplifications were included by their au- thors. These simplifications are mainly due to the insignificant influence of the modelled phenom-
g.6. Timeline of development of velocity storage, observer and optimal control perception models [87]. Over the past several decades, mathematical models of the human perception of spatial ori- entation based on the concept derived from the estimation theory have been widely used in ves- tibular system studies. Most of these models use one of the three estimation techniques: Bayesian estimation [60,63,68], Kalman’s filter (KF)[11,13,103] with its variations [9,116] and the state observer In the models of spatial orientation perception a some simplifications were included by their au- thors. These simplifications are mainly due to the insignificant influence of the modelled phenom-
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PerceptionHuman Perception and PerformanceModeling and SimulationMotion perceptionPerception of Self-Motion
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