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 Biological Cybernetics   [SJR: 0.856]   [H-I: 76]   [10 followers]  Follow         Hybrid journal (It can contain Open Access articles)    ISSN (Print) 1432-0770 - ISSN (Online) 0340-1200    Published by Springer-Verlag  [2351 journals]
• Place recognition from distant landmarks: human performance and maximum
likelihood model
• Authors: Hanspeter A. Mallot; Stephan Lancier
Abstract: We present a simple behavioral experiment on human place recognition from a configuration of four visual landmarks. Participants were asked to navigate several paths, all involving a turn at one specific point, and while doing so incidentally learned the position of that turning point. In the test phase, they were asked to return to the turning point in a reduced environment leaving only the four landmarks visible. Results are compared to two versions of a maximum likelihood model of place recognition using either view-based or depth-based cues for place recognition. Only the depth-based model is in good qualitative agreement with the data. In particular, it reproduces landmark configuration-dependent effects of systematic bias and statistical error distribution as well as effects of approach direction. The model is based on a place code (depth and bearing of the landmarks at target location) and an egocentric working memory of surrounding space including current landmark position in a local, map-like representation. We argue that these elements are crucial for human place recognition.
PubDate: 2018-02-26
DOI: 10.1007/s00422-018-0751-4

• Sustained sensorimotor control as intermittent decisions about prediction
errors: computational framework and application to ground vehicle steering

• Authors: Gustav Markkula; Erwin Boer; Richard Romano; Natasha Merat
Abstract: A conceptual and computational framework is proposed for modelling of human sensorimotor control and is exemplified for the sensorimotor task of steering a car. The framework emphasises control intermittency and extends on existing models by suggesting that the nervous system implements intermittent control using a combination of (1) motor primitives, (2) prediction of sensory outcomes of motor actions, and (3) evidence accumulation of prediction errors. It is shown that approximate but useful sensory predictions in the intermittent control context can be constructed without detailed forward models, as a superposition of simple prediction primitives, resembling neurobiologically observed corollary discharges. The proposed mathematical framework allows straightforward extension to intermittent behaviour from existing one-dimensional continuous models in the linear control and ecological psychology traditions. Empirical data from a driving simulator are used in model-fitting analyses to test some of the framework’s main theoretical predictions: it is shown that human steering control, in routine lane-keeping and in a demanding near-limit task, is better described as a sequence of discrete stepwise control adjustments, than as continuous control. Results on the possible roles of sensory prediction in control adjustment amplitudes, and of evidence accumulation mechanisms in control onset timing, show trends that match the theoretical predictions; these warrant further investigation. The results for the accumulation-based model align with other recent literature, in a possibly converging case against the type of threshold mechanisms that are often assumed in existing models of intermittent control.
PubDate: 2018-02-16
DOI: 10.1007/s00422-017-0743-9

• Coupling relations underlying the production of speech articulator
movements and their invariance to speech rate
• Authors: Leonardo Lancia; Benjamin Rosenbaum
Abstract: Since the seminal works of Bernstein (The coordination and regulation of movements. Pergamon Press, Oxford, 1967) several authors have supported the idea that, to produce a goal-oriented movement in general, and a movement of the organs responsible for the production of speech sounds in particular, individuals activate a set of coupling relations that coordinate the behavior of the elements of the motor system involved in the production of the target movement or sound. In order to characterize the configurations of the coupling relations underlying speech production articulator movements, we introduce an original method based on recurrence analysis. The method is validated through the analysis of simulated dynamical systems adapted to reproduce the features of speech gesture kinematics and it is applied to the analysis of speech articulator movements recorded in five German speakers during the production of labial and coronal plosive and fricative consonants at variable speech rates. We were able to show that the underlying coupling relations change systematically between labial and coronal consonants, but are not affected by speech rate, despite the presence of qualitative changes observed in the trajectory of the jaw at fast speech rate.
PubDate: 2018-02-09
DOI: 10.1007/s00422-018-0749-y

• Bipedal robotic walking control derived from analysis of human locomotion
• Authors: Lin Meng; Catherine A. Macleod; Bernd Porr; Henrik Gollee
Abstract: This paper proposes the design of a bipedal robotic controller where the function between the sensory input and motor output is treated as a black box derived from human data. In order to achieve this, we investigated the causal relationship between ground contact information from the feet and leg muscle activity n human walking and calculated filter functions which transform sensory signals to motor actions. A minimal, nonlinear, and robust control system was created and subsequently analysed by applying it to our bipedal robot RunBot III without any central pattern generators or precise trajectory control. The results demonstrate that our controller can generate stable robotic walking. This indicates that complex locomotion patterns can result from a simple model based on reflexes and supports the premise that human-derived control strategies have potential applications in robotics or assistive devices.
PubDate: 2018-02-05
DOI: 10.1007/s00422-018-0750-5

• A parsimonious model of brightness induction
• Authors: Ashish Bakshi; Kuntal Ghosh
Abstract: We present a parsimonious model of brightness induction which can account for various brightness illusions of both brightness-contrast and brightness-assimilation types. Our model is based on a difference of difference-of-Gaussian filter and a two-pass model of attentive vision based on the parallel channels in the central visual pathway. It overcomes some of the problems that could not be addressed by the well-known oriented difference of Gaussian model like those associated with Mach band and checkerboard illusions. This model attempts to provide insight to the mechanism of attention in brightness perception through the two major complimentary visual channels, viz. the magnocellular and the parvocellular.
PubDate: 2018-01-22
DOI: 10.1007/s00422-018-0747-0

• Leveraging variable sensor spatial acuity with a homogeneous, multi-scale
place recognition framework
• Authors: Adam Jacobson; Zetao Chen; Michael Milford
Abstract: Most robot navigation systems perform place recognition using a single-sensor modality and one, or at most two heterogeneous map scales. In contrast, mammals perform navigation by combining sensing from a wide variety of modalities including vision, auditory, olfactory and tactile senses with a multi-scale, homogeneous neural map of the environment. In this paper, we develop a multi-scale, multi-sensor system for mapping and place recognition that combines spatial localization hypotheses at different spatial scales from multiple different sensors to calculate an overall place recognition estimate. We evaluate the system’s performance over three repeated 1.5-km day and night journeys across a university campus spanning outdoor and multi-level indoor environments, incorporating camera, WiFi and barometric sensory information. The system outperforms a conventional camera-only localization system, with the results demonstrating not only how combining multiple sensing modalities together improves performance, but also how combining these sensing modalities over multiple scales further improves performance over a single-scale approach. The multi-scale mapping framework enables us to analyze the naturally varying spatial acuity of different sensing modalities, revealing how the multi-scale approach captures each sensing modality at its optimal operation point where a single-scale approach does not, and enables us to then weight sensor contributions at different scales based on their utility for place recognition at that scale.
PubDate: 2018-01-20
DOI: 10.1007/s00422-017-0745-7

• A cardioid oscillator with asymmetric time ratio for establishing CPG
models
• Authors: Q. Fu; D. H. Wang; L. Xu; G. Yuan
Abstract: Nonlinear oscillators are usually utilized by bionic scientists for establishing central pattern generator models for imitating rhythmic motions by bionic scientists. In the natural word, many rhythmic motions possess asymmetric time ratios, which means that the forward and the backward motions of an oscillating process sustain different times within one period. In order to model rhythmic motions with asymmetric time ratios, nonlinear oscillators with asymmetric forward and backward trajectories within one period should be studied. In this paper, based on the property of the invariant set, a method to design the closed curve in the phase plane of a dynamic system as its limit cycle is proposed. Utilizing the proposed method and considering that a cardioid curve is a kind of asymmetrical closed curves, a cardioid oscillator with asymmetric time ratios is proposed and realized. Through making the derivation of the closed curve in the phase plane of a dynamic system equal to zero, the closed curve is designed as its limit cycle. Utilizing the proposed limit cycle design method and according to the global invariant set theory, a cardioid oscillator applying a cardioid curve as its limit cycle is achieved. On these bases, the numerical simulations are conducted for analyzing the behaviors of the cardioid oscillator. The example utilizing the established cardioid oscillator to simulate rhythmic motions of the hip joint of a human body in the sagittal plane is presented. The results of the numerical simulations indicate that, whatever the initial condition is and without any outside input, the proposed cardioid oscillator possesses the following properties: (1) The proposed cardioid oscillator is able to generate a series of periodic and anti-interference self-exciting trajectories, (2) the generated trajectories possess an asymmetric time ratio, and (3) the time ratio can be regulated by adjusting the oscillator’s parameters. Furthermore, the comparison between the simulated trajectories by the established cardioid oscillator and the measured angle trajectories of the hip angle of a human body show that the proposed cardioid oscillator is fit for imitating the rhythmic motions of the hip of a human body with asymmetric time ratios.
PubDate: 2018-01-13
DOI: 10.1007/s00422-018-0746-1

• Editorial Board of Biological Cybernetics: Advances in Computational
Neuroscience
• PubDate: 2018-01-12
DOI: 10.1007/s00422-017-0744-8

• Identification of optimal feedback control rules from micro-quadrotor and
insect flight trajectories
• Authors: Imraan A. Faruque; Florian T. Muijres; Kenneth M. Macfarlane; Andrew Kehlenbeck; J. Sean Humbert
Abstract: This paper presents “optimal identification,” a framework for using experimental data to identify the optimality conditions associated with the feedback control law implemented in the measurements. The technique compares closed loop trajectory measurements against a reduced order model of the open loop dynamics, and uses linear matrix inequalities to solve an inverse optimal control problem as a convex optimization that estimates the controller optimality conditions. In this study, the optimal identification technique is applied to two examples, that of a millimeter-scale micro-quadrotor with an engineered controller on board, and the example of a population of freely flying Drosophila hydei maneuvering about forward flight. The micro-quadrotor results show that the performance indices used to design an optimal flight control law for a micro-quadrotor may be recovered from the closed loop simulated flight trajectories, and the Drosophila results indicate that the combined effect of the insect longitudinal flight control sensing and feedback acts principally to regulate pitch rate.
PubDate: 2018-01-03
DOI: 10.1007/s00422-017-0742-x

• Analysis of fMRI data using noise-diffusion network models: a new
covariance-coding perspective
• Authors: Matthieu Gilson
Abstract: Since the middle of the 1990s, studies of resting-state fMRI/BOLD data have explored the correlation patterns of activity across the whole brain, which is referred to as functional connectivity (FC). Among the many methods that have been developed to interpret FC, a recently proposed model-based approach describes the propagation of fluctuating BOLD activity within the recurrently connected brain network by inferring the effective connectivity (EC). In this model, EC quantifies the strengths of directional interactions between brain regions, viewed from the proxy of BOLD activity. In addition, the tuning procedure for the model provides estimates for the local variability (input variances) to explain how the observed FC is generated. Generalizing, the network dynamics can be studied in the context of an input–output mapping—determined by EC—for the second-order statistics of fluctuating nodal activities. The present paper focuses on the following detection paradigm: observing output covariances, how discriminative is the (estimated) network model with respect to various input covariance patterns' An application with the model fitted to experimental fMRI data—movie viewing versus resting state—illustrates that changes in local variability and changes in brain coordination go hand in hand.
PubDate: 2017-12-04
DOI: 10.1007/s00422-017-0741-y

• The role of neuron–glia interactions in the emergence of ultra-slow
oscillations
• Authors: Siow-Cheng Chan; Siew-Ying Mok; Danny Wee-Kiat Ng; Sing-Yau Goh
Abstract: Ultra-slow cortical oscillatory activity of 1–100 mHz has been recorded in human by electroencephalography and in dissociated cultures of cortical rat neurons, but the underlying mechanisms remain to be elucidated. This study presents a computational model of ultra-slow oscillatory activity based on the interaction between neurons and astrocytes. We predict that the frequency of these oscillations closely depends on activation of astrocytes in the network, which is reflected by oscillations of their intracellular calcium concentrations with periods between tens of seconds and minutes. An increase of intracellular calcium in astrocytes triggers the release of adenosine triphosphate from these cells which may alter transmission at nearby synapses by increasing or decreasing neurotransmitter release. These results provide theoretical support for the emerging awareness of astrocytes as active players in the regulation of neural activity and identify neuron–astrocyte interactions as a potential primary mechanism for the emergence of ultra-slow cortical oscillations.
PubDate: 2017-11-11
DOI: 10.1007/s00422-017-0740-z

• Area-specific processing of cerebellar-thalamo-cortical information in
primates
• Authors: Abdulraheem Nashef; Hannes Rapp; Martin P. Nawrot; Yifat Prut
Abstract: The cerebellar-thalamo-cortical (CTC) system plays a major role in controlling timing and coordination of voluntary movements. However, the functional impact of this system on motor cortical sites has not been documented in a systematic manner. We addressed this question by implanting a chronic stimulating electrode in the superior cerebellar peduncle (SCP) and recording evoked multiunit activity (MUA) and the local field potential (LFP) in the primary motor cortex ( $$n=926$$ ), the premotor cortex ( $$n=357$$ ) and the somatosensory cortex ( $$n=345$$ ). The area-dependent response properties were estimated using the MUA response shape (quantified by decomposing into principal components) and the time-dependent frequency content of the evoked LFP. Each of these signals alone enabled good classification between the somatosensory and motor sites. Good classification between the primary motor and premotor areas could only be achieved when combining features from both signal types. Topographical single-site representation of the predicted class showed good recovery of functional organization. Finally, the probability for misclassification had a broad topographical organization. Despite the area-specific response features to SCP stimulation, there was considerable site-to-site variation in responses, specifically within the motor cortical areas. This indicates a substantial SCP impact on both the primary motor and premotor cortex. Given the documented involvement of these cortical areas in preparation and execution of movement, this result may suggest a CTC contribution to both motor execution and motor preparation. The stimulation responses in the somatosensory cortex were sparser and weaker. However, a functional role of the CTC system in somatosensory computation must be taken into consideration.
PubDate: 2017-11-02
DOI: 10.1007/s00422-017-0738-6

• Analysis of a purely conductance-based stochastic nerve fibre model as
applied to compound models of populations of human auditory nerve fibres
used in cochlear implant simulations
• Authors: Werner Badenhorst; Tania Hanekom; Johan J. Hanekom
Abstract: The study presents the application of a purely conductance-based stochastic nerve fibre model to human auditory nerve fibres within finite element volume conduction models of a semi-generic head and user-specific cochleae. The stochastic, threshold and temporal characteristics of the human model are compared and successfully validated against physiological feline results with the application of a mono-polar, bi-phasic, cathodic first stimulus. Stochastic characteristics validated include: (i) the log(Relative Spread) versus log(fibre diameter) distribution for the discharge probability versus stimulus intensity plots and (ii) the required exponential membrane noise versus transmembrane voltage distribution. Intra-user, and to a lesser degree inter-user, comparisons are made with respect to threshold and dynamic range at short and long pulse widths for full versus degenerate single fibres as well as for populations of degenerate fibres of a single user having distributed and aligned somas with varying and equal diameters. Temporal characteristics validated through application of different stimulus pulse rates and different stimulus intensities include: (i) discharge rate, latency and latency standard deviation versus stimulus intensity, (ii) period histograms and (iii) interspike interval histograms. Although the stochastic population model does not reduce the modelled single deterministic fibre threshold, the simulated stochastic and temporal characteristics show that it could be used in future studies to model user-specific temporally encoded information, which influences the speech perception of CI users.
PubDate: 2017-10-24
DOI: 10.1007/s00422-017-0736-8

• Fuzzy neuronal model of motor control inspired by cerebellar pathways to
online and gradually learn inverse biomechanical functions in the presence
of delay
Abstract: Contrary to forward biomechanical functions, which are deterministic, inverse biomechanical functions are generally not. Calculating an inverse biomechanical function is an ill-posed problem, which has no unique solution for a manipulator with several degrees of freedom. Studies of the command and control of biological movements suggest that the cerebellum takes part in the computation of approximate inverse functions, and this ability can control fast movements by predicting the consequence of current motor command. Limb movements toward a goal are defined as fast if they last less than the total duration of the processing and transmission delays in the motor and sensory pathways. Because of these delays, fast movements cannot be continuously controlled in a closed loop by use of sensory signals. Thus, fast movements must be controlled by some open loop controller, of which cerebellar pathways constitute an important part. This article presents a system-level fuzzy neuronal motor control circuit, inspired by the cerebellar pathways. The cerebellar cortex (CC) is assumed to embed internal models of the biomechanical functions of the limb segments. Such neural models are able to predict the consequences of motor commands and issue predictive signals encoding movement variables, which are sent to the controller via internal feedback loops. Differences between desired and expected values of variables of movements are calculated in the deep cerebellar nuclei (DCN). After motor learning, the whole circuit can approximate the inverse function of the biomechanical function of a limb and acts as a controller. In this research, internal models of direct biomechanical functions are learned and embedded in the connectivity of the cerebellar pathways. Two fuzzy neural networks represent the two parts of the cerebellum, and an online gradual learning drives the acquisition of the internal models in CC and the controlling rules in DCN. As during real learning, exercise and repetition increase skill and speed. The learning procedure is started by a simple and slow movement, controlled in the presence of delays by a simple closed loop controller comparable to the spinal reflexes. The speed of the movements is then increased gradually, and output error signals are used to compute teaching signals and drive learning. Repetition of movements at each speed level allows to properly set the two neural networks, and progressively learn the movement. Finally, conditions of stability of the proposed model as an inverter are identified. Next, the control of a single segment arm, moved by two muscles, is simulated. After proper setting by motor learning, the circuit is able to reject perturbations.
PubDate: 2017-10-09
DOI: 10.1007/s00422-017-0735-9

• ‘Two vs one’ rivalry by the Loxley–Robinson model
• Authors: August Romeo; Hans Supèr
Abstract: We apply the competitive model of Loxley and Robinson (Phys Rev Lett 102:258701, 2009. doi:10.1103/PhysRevLett.102.258701) to the study of a special case of visual rivalry. Three-peaked inputs with maxima at symmetrical locations are introduced, and the role of three-bump configurations is then considered. The model yields conditions for what can be interpreted as a bistable percept analogous to the one-dimensional version of a competition between the central and flanking parts of an image.
PubDate: 2017-09-19
DOI: 10.1007/s00422-017-0734-x

• A multi-joint model of quiet, upright stance accounts for the
“uncontrolled manifold” structure of joint variance
• Authors: Hendrik Reimann; Gregor Schöner
Abstract: The upright body in quiet stance is usually modeled as a single-link inverted pendulum. This agrees with most of the relevant sensory organs being at the far end of the pendulum, i.e., the eyes and the vestibular system in the head. Movement of the body in quiet stance has often been explained in terms of the “ankle strategy,” where most movement is generated by the ankle musculature, while more proximal muscle groups are only rarely activated for faster movements or in response to perturbations, for instance, by flexing at the hips in what has been called the “hip strategy.” Recent empirical evidence, however, shows that instead of being negligible in quiet stance, the movement in the knee and hip joints is even larger on average than the movement in the ankle joints (J Neurophysiol 97:3024-3035, 2007). Moreover, there is a strong pattern of covariation between movements in the ankle, knee and hip joints in a way that most of the observed movements leave the anterior–posterior position of the whole-body center of mass (CoM) invariant, i.e., only change the configuration of the different body parts around the CoM, instead of moving the body as a whole. It is unknown, however, where this covariation between joint angles during quiet stance originates from. In this paper, we aim to answer this question using a comprehensive model of the biomechanical, muscular and neural dynamics of a quietly standing human. We explore four different possible feedback laws for the control of this multi-link pendulum in upright stance that map sensory data to motor commands. We perform simulation studies to compare the generated inter-joint covariance patterns with experimental data. We find that control laws that actively coordinate muscle activation between the different joints generate correct variance patterns, while control laws that control each joint separately do not. Different specific forms of this coordination are compatible with the data.
PubDate: 2017-09-18
DOI: 10.1007/s00422-017-0733-y

• Affective–associative two-process theory: a neurocomputational account
of partial reinforcement extinction effects
• Authors: Robert Lowe; Alexander Almér; Erik Billing; Yulia Sandamirskaya; Christian Balkenius
Abstract: The partial reinforcement extinction effect (PREE) is an experimentally established phenomenon: behavioural response to a given stimulus is more persistent when previously inconsistently rewarded than when consistently rewarded. This phenomenon is, however, controversial in animal/human learning theory. Contradictory findings exist regarding when the PREE occurs. One body of research has found a within-subjects PREE, while another has found a within-subjects reversed PREE (RPREE). These opposing findings constitute what is considered the most important problem of PREE for theoreticians to explain. Here, we provide a neurocomputational account of the PREE, which helps to reconcile these seemingly contradictory findings of within-subjects experimental conditions. The performance of our model demonstrates how omission expectancy, learned according to low probability reward, comes to control response choice following discontinuation of reward presentation (extinction). We find that a PREE will occur when multiple responses become controlled by omission expectation in extinction, but not when only one omission-mediated response is available. Our model exploits the affective states of reward acquisition and reward omission expectancy in order to differentially classify stimuli and differentially mediate response choice. We demonstrate that stimulus–response (retrospective) and stimulus–expectation–response (prospective) routes are required to provide a necessary and sufficient explanation of the PREE versus RPREE data and that Omission representation is key for explaining the nonlinear nature of extinction data.
PubDate: 2017-09-14
DOI: 10.1007/s00422-017-0730-1

• Conductance-based refractory density approach: comparison with
experimental data and generalization to lognormal distribution of input
current
• Authors: Anton V. Chizhov
Abstract: The conductance-based refractory density (CBRD) approach is an efficient tool for modeling interacting neuronal populations. The model describes the firing activity of a statistical ensemble of uncoupled Hodgkin–Huxley-like neurons, each receiving individual Gaussian noise and a common time-varying deterministic input. However, the approach requires experimental validation and extension to cases of distributed input signals (or input weights) among different neurons of such an ensemble. Here the CBRD model is verified by comparing with experimental data and then generalized for a lognormal (LN) distribution of the input weights. The model with equal weights is shown to reproduce efficiently the post-spike time histograms and the membrane voltage of experimental multiple trial response of single neurons to a step-wise current injection. The responses reveal a more rapid reaction of the firing-rate than voltage. Slow adaptive potassium channels strongly affected the shape of the responses. Next, a computationally efficient CBRD model is derived for a population with the LN input weight distribution and is compared with the original model with equal input weights. The analysis shows that the LN distribution: (1) provides a faster response, (2) eliminates oscillations, (3) leads to higher sensitivity to weak stimuli, and (4) increases the coefficient of variation of interspike intervals. In addition, a simplified firing-rate type model is tested, showing improved precision in the case of a LN distribution of weights. The CBRD approach is recommended for complex, biophysically detailed simulations of interacting neuronal populations, while the modified firing-rate type model is recommended for computationally reduced simulations.
PubDate: 2017-08-17
DOI: 10.1007/s00422-017-0727-9

• A model of the FAD redox cycle describes the dynamics of the effect of the
geomagnetic field on the human visual system
• Authors: Franz Thoss; Bengt Bartsch
Abstract: In experimental studies, we could show that the visual threshold of man is influenced by the geomagnetic field. One of the results was that the threshold shows periodic fluctuations when the vertical component of the field is reversed periodically. The maximum of these oscillations occurred at a period duration of 110 s. To explain this phenomenon, we chose the process that likely underlies the navigation of birds in the geomagnetic field: the light reaction of the FAD component of cryptochrome in the retina. The human retina contains cryptpochrome like the bird retina. Based on the investigations of Müller and Ahmad (J Biol Chem 286:21033–21040, 2011) and Solov’yov and Schulten (J Phys Chem B 116:1089–1099, 2012), we designed a model of the light-induced reduction and subsequent reoxidation of FAD. This model contains a radical pair, whose interconversion dynamics are affected by the geomagnetic field. The parameters of the model were partly calculated from the data of our experimental investigation and partly taken from the results of other authors. These parameters were then optimized by adjusting the model behaviour to the experimental results. The simulation of the finished model shows that the concentrations of all substances included show really oscillations with the frequency of the modelled magnetic field. After optimization of the parameters, the oscillations of FAD and FADH* show maximal amplitude at a period duration of 110 s, as was observed in the experiment. This makes it most likely that the signal, which influences the visual system, originates from FADH* (signalling state).
PubDate: 2017-08-03
DOI: 10.1007/s00422-017-0725-y

• Real-time muscle state estimation from EMG signals during isometric
contractions using Kalman filters
• Authors: Luciano L. Menegaldo
Abstract: State-space control of myoelectric devices and real-time visualization of muscle forces in virtual rehabilitation require measuring or estimating muscle dynamic states: neuromuscular activation, tendon force and muscle length. This paper investigates whether regular (KF) and extended Kalman filters (eKF), derived directly from Hill-type muscle mechanics equations, can be used as real-time muscle state estimators for isometric contractions using raw electromyography signals (EMG) as the only available measurement. The estimators’ amplitude error, computational cost, filtering lags and smoothness are compared with usual EMG-driven analysis, performed offline, by integrating the nonlinear Hill-type muscle model differential equations (offline simulations—OS). EMG activity of the three triceps surae components (soleus, gastrocnemius medialis and gastrocnemius lateralis), in three torque levels, was collected for ten subjects. The actualization interval (AI) between two updates of the KF and eKF was also varied. The results show that computational costs are significantly reduced (70x for KF and 17 $$\times$$ for eKF). The filtering lags presented sharp linear relationships with the AI (0–300 ms), depending on the state and activation level. Under maximum excitation, amplitude errors varied in the range 10–24% for activation, 5–8% for tendon force and 1.4–1.8% for muscle length, reducing linearly with the excitation level. Smoothness, measured by the ratio between the average standard variations of KF/eKF and OS estimations, was greatly reduced for activation but converged exponentially to 1 for the other states by increasing AI. Compared to regular KF, extended KF does not seem to improve estimation accuracy significantly. Depending on the particular application requirements, the most appropriate KF actualization interval can be selected.
PubDate: 2017-08-01
DOI: 10.1007/s00422-017-0724-z

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