Adaptive intermittent control: A computational model explaining motor intermittency observed in human behavior

It is a fundamental question how our brain performs a given motor task in a real-time fashion with the slow sensorimotor system. Computational theory proposed an influential idea of feed-forward control, but it has mainly treated the case that the movement is ballistic (such as reaching) because the motor commands should be calculated in advance of movement execution. As a possible mechanism for operating feed-forward control in continuous motor tasks (such as target tracking), we propose a control model called "adaptive intermittent control" or "segmented control," that brain adaptively divides the continuous time axis into discrete segments and executes feed-forward control in each segment. The idea of intermittent control has been proposed in the fields of control theory, biological modeling and nonlinear dynamical system. Compared with these previous models, the key of the proposed model is that the system speculatively determines the segmentation based on the future prediction and its uncertainty. The result of computer simulation showed that the proposed model realized faithful visuo-manual tracking with realistic sensorimotor delays and with less computational costs (i.e., with fewer number of segments). Furthermore, it replicated "motor intermittency", that is, intermittent discontinuities commonly observed in human movement trajectories. We discuss that the temporally segmented control is an inevitable strategy for brain which has to achieve a given task with small computational (or cognitive) cost, using a slow control system in an uncertain variable environment, and the motor intermittency is the side-effect of this strategy

Wavelet-frequency analysis for the detection of discontinuities in switched system models of human balance

This paper is concerned with detecting the presence of switching behavior in experimentally obtained posturographic data sets by means of a novel algorithm that is based on a combination of wavelet analysis and Hilbert transform. As a test-bed for the algorithm, we first use a switched model of human balance control during quiet standing with known switching behavior in four distinct configurations. We obtain a time-frequency representation of a signal generated by our model system. We are then able to detect manifestations of discontinuities (switchings) in the signal as spiking behavior. The frequency of switchings, measured by means of our algorithm and detected in our models systems, agrees with the frequency of spiking behavior found in the experimentally obtained posturographic data

Analytical methods and experimental approaches for electrophysiological studies of brain oscillations

Brain oscillations are increasingly the subject of electrophysiological studies probing their role in the functioning and dysfunction of the human brain. In recent years this research area has seen rapid and significant changes in the experimental approaches and analysis methods. This article reviews these developments and provides a structured overview of experimental approaches, spectral analysis techniques and methods to establish relationships between brain oscillations and behaviour

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

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

NOVEL METHODS FOR PERMANENT MAGNET DEMAGNETIZATION DETECTION IN PERMANENT MAGNET SYNCHRONOUS MACHINES

Monitoring and detecting PM flux linkage is important to maintain a stable permanent magnet synchronous motor (PMSM) operation. The key problems that need to be solved at this stage are to: 1) establish a demagnetization magnetic flux model that takes into account the influence of various nonlinear and complex factors to reveal the demagnetization mechanism; 2) explore the relationship between different factors and demagnetizing magnetic field, to detect the demagnetization in the early stage; and 3) propose post-demagnetization measures. This thesis investigates permanent magnet (PM) demagnetization detection for PMSM machines to achieve high-performance and reliable machine drive for practical industrial and consumer applications. In this thesis, theoretical analysis, numerical calculation as well as experimental investigations are carried out to systematically study the demagnetization detection mechanism and post-demagnetization measures for permanent magnet synchronous motors. At first a flux based acoustic noise model is proposed to analyze online PM demagnetization detection by using a back propagation neural network (BPNN) with acoustic noise data. In this method, the PM demagnetization is detected by means of comparing the measured acoustic signal of PMSM with an acoustic signal library of seven acoustical indicators. Then torque ripple is chosen for online PM demagnetization diagnosis by using continuous wavelet transforms (CWT) and Grey System Theory (GST). This model is able to reveal the relationship between torque variation and PM electromagnetic interferences. After demagnetization being detected, a current regulation strategy is proposed to minimize the torque ripples induced by PM demagnetization. Next, in order to compare the demagnetization detection accuracy, different data mining techniques, Vold-Kalman filtering order tracking (VKF-OT) and dynamic Bayesian network (DBN) based detection approach is applied to real-time PM flux monitoring through torque ripple again. VKF-OT is introduced to track the order of torque ripple of PMSM running in transient state. Lastly, the combination of acoustic noise and torque is investigated for demagnetization detection by using multi-sensor information fusion to improve the system redundancy and accuracy. Bayesian network based multi-sensor information fusion is then proposed to detect the demagnetization ratio from the extracted features. During the analysis of demagnetization detection methods, the proposed PM detection approaches both form torque ripple and acoustic noise are extensively evaluated on a laboratory PM machine drive system under different speeds, load conditions, and temperatures

Neuromuscular Control Modeling: from Physics to Data-Driven Approaches

Il controllo neurale della postura umana è stato investigato a partire da un punto di vista fisico. Il paradigma di controllo intermittente è stato usato allo scopo di capire il peso di quest'ultimo nella generazione delle traiettorie del centro di pressione. Un primo contributo di questo lavoro rigurda quindi l'analisi del centro di pressione generato dal suddetto modello biomeccanico attraverso l'extended detrended fluctuation analysis, recentemente proposta in letteratura. Le proprietà di correlazione a lungo termine e disomogeneità sono risultate strettamente legate al guadagno derivativo del modello di controllo intermittente e anche al grado di intermittenza. Il paradigma di controllo è stato poi esteso verso un sistema biomeccanico più complesso, cioè un pendolo inverso doppio link con controllo intermittente alla caviglia. I contributi più significativi hanno riguardato la modellazione matematica del centro di pressione per una struttura multi-link e la verifica della sua plausibilità fisiologica. Si è poi preso in considerazione il caso della postura perturbata, integrando aspetti cinematici, dinamici e relativi all'attività muscolare. A tal fine, si è utilizzato sia un approccio fisico che basato su dati per l'identificazione dei modelli a struttura variabile Si sono prese in considerazione differenti condizioni di sperimentali, e in tutti i casi l'approccio utilizzato ha garantito un adeguato grado di interpretabilità riguardo il ruolo del sistema nervoso centrale nella regolazione del postura eretta in condizioni perturbate. La seconda parte della tesi ha riguardato la caratterizzazione del controllo motorio attraverso il segnale elettromiografico di superfice. Il primo contributo ha riguardato l'identifcazione dell'onset muscolare in condizioni di basso rapporto segnale rumore, sfruttando operatori energetico di tipo Teager-Kaiser al fine del precondizionamento del segnale mioelettrico. La versioe estesa di questo tipo di operatori è risultata particolarmente utile al miglioramento delle performance di numerosi algoritmi di detection. Si è poi proseguito con l'utilizzo di tali segnali al fine della classificazione dei gesti dell'arto superiore. In particoalre si è prerso in considerazione il problema della motion intention detection dei principali movimenti della spalla , utilizzando sia descrittori del segnale elettromiografico nel dominio del tempo e della frequenza. Quest'ultimo aspetto risulta essere un elemento di novità nel contesto scientifico in quanto si sono considerati il riconoscimento l'intezioni di movimento di otto gesti della spalla con particolare attenzione al ruolo dei descrittori del segnale per la classificazione. Infine, con approcci simili, si è preso in considerazione il problema del riconoscimento della scrittura manuale a partire dal dato elettromiografico. Tale aspetto risulta poco investigato sotto la prospettiva della pattern recognition mioelettrica, ma la sua valenza è data dalla crescente richiesta di interfacce uomo-macchina per compiti riabilitativi che coinvolgono una componente cognitiva significativa, Inoltre, vista la tendenza ad investigare il ruolo del polso per il prelievo del segnale elettromiografico al fine della realizzazione delle suddette interfacce, si è analizzato l'utilizzo dei segnali elettromiografici del polso rispetto a quelli dell'avambraccio al fine di predirre le cifre scritte dall'utente, noto che l'avambraccio risulta essere la zona di prelievo più comunemente utilizzata.The biomechanics and the neural control of the human stance was investigated starting from a physical point of view. In particular the intermittent motor control paradigm was investigated with the aim of understanding how such paradigm mirrors in the center of pressure (COP) trajectories. A first contribution given in this work of thesis regards the analysis of COP generated from intermittent controlled inverted pendulum through the extended detrended fluctuation analysis, which was recently introduced in the literature. It has been found that the long-term correlation and inohmogeniety properties of the COP time series strictly depend on the derivative gain term of the intermittent controller and on the degree of intermittency of the control action. Thus, , another contribution provided in this work of thesis regards the use of a more complex biomechanical model of the stance, e.g. a double-link inverted pendulum intermittently controlled at the ankle. In terms of novelty, it deserves to be pointed out the results regarding the mechanical modeling of the COP for a multi-link structure, and the assessment of its physiological plausibility. . On the other hand, when the perturbed posture motor task was taken into account, there was the need to enlarge the perspective, integrating kinematic, dynamic and muscle activity data. The idea of employing different sources of information to develop models of the CNS represents an important element that was investigated using tools related to hybrid system identification theory. Subjects underwent to impulsive support base translations in three different conditions: considering eyes open, closed, and performing mental counting. Although such data were essentially analyzed through a data-driven approach, the identified models guaranteed physical interpretations of the role played by the CNS in the three different conditions. The second main core of this thesis regards the characterization of the motor control using the surface electromyographic (sEMG) signals. A first contribution given in this work regarded the muscle onset detection considering low SNR scenarios. In this framework, energy operators such as the Teager-Kaiser energy operator (TKEO) and its extended version (ETKEO) were investigated as signal preconditioning steps before the application of state of the art onset detection algorithms. The latter have been significantly boosted when ETKEO was used with respect to TKEO. The use of extended energy operators for the sEMG signal preprocessing constitutes a novel element in this field that can be also further investigated in future studies. From the sEMG muscle, one can also predict which movement the subject is going to perform. This aspect can be enclosed in the motion intention detection (MID) field. In this thesis a MID problem was investigated by taking into account two important aspects: as first the study was centered on the shoulder joint movements. Secondly, the MID problem was faced under a pattern recognition perspective. This allowed to verify whether methodologies encountered in the myoelectric hand gesture recognition can be transferred in the affine field of MID In contrast to what reported in the literature, where MID problems generally consider only few movements, in this work of thesis up to eight shoulder movements have been investigated. Myoelectric pattern recognition architectures were also used in the assessment of the ten hand-written digits. Despite the handwriting can be considered a hand movement that involves fine muscular control actions, it has not been consistently investigated in the field of sEMG based hand gesture recognition. Further, since the literature supports the change from forearm to wrist in order to acquire EMG data for hand gesture recognition, it was investigated whether such exchange can be performed when a challenging classification task, as the handwriting recognition has to be performed

Prognostic Approaches Using Transient Monitoring Methods

The utilization of steady state monitoring techniques has become an established means of providing diagnostic and prognostic information regarding both systems and equipment. However, steady state data is not the only, or in some cases, even the best source of information regarding the health and state of a system. Transient data has largely been overlooked as a source of system information due to the additional complexity in analyzing these types of signals. The development for algorithms and techniques to quickly, and intuitively develop generic quantification of deviations a transient signal towards the goal of prognostic predictions has until now, largely been overlooked. By quantifying and trending these shifts, an accurate measure of system heath can be established and utilized by prognostic algorithms. In fact, for some systems the elevated stress levels during transients can provide better, more clear indications of system health than those derived from steady state monitoring. This research is based on the hypothesis that equipment health signals for some failure modes are stronger during transient conditions than during steady-state because transient conditions (e.g. start-up) place greater stress on the equipment for these failure modes. From this it follows that these signals related to the system or equipment health would display more prominent indications of abnormality if one were to know the proper means to identify them. This project seeks to develop methods and conceptual models to monitor transient signals for equipment health. The purpose of this research is to assess if monitoring of transient signals could provide alternate or better indicators of incipient equipment failure prior to steady state signals. The project is focused on identifying methods, both traditional and novel, suitable to implement and test transient model monitoring in both an useful and intuitive way. By means of these techniques, it is shown that the addition information gathered during transient portions of life can be used to either to augment existing steady-state information, or in cases where such information is unavailable, be used as a primary means of developing prognostic models

AUTOMATIC DETECTION OF SLEEP AND WAKE STATES IN MICE USING PIEZOELECTRIC SENSORS

Currently technologies such as EEG, EMG and EOG recordings are the established methods used in the analysis of sleep. But if these methods are to be employed to study sleep in rodents, extensive surgery and recovery is involved which can be both time consuming and costly. This thesis presents and analyzes a cost effective, non-invasive, high throughput system for detecting the sleep and wake patterns in mice using a piezoelectric sensor. This sensor was placed at the bottom of the mice cages to monitor the movements of the mice. The thesis work included the development of the instrumentation and signal acquisition system for recording the signals critical to sleep and wake classification. Classification of the mouse sleep and wake states were studied for a linear classifier and a Neural Network classifier based on 23 features extracted from the Power Spectrum (PS), Generalized Spectrum (GS), and Autocorrelation (AC) functions of short data intervals. The testing of the classifiers was done on two data sets collected from two mice, with each data set having around 5 hours of data. A scoring of the sleep and wake states was also done via human observation to aid in the training of the classifiers. The performances of these two classifiers were analyzed by looking at the misclassification error of a set of test features when run through a classifier trained by a set of training features. The best performing features were selected by first testing each of the 23 features individually in a linear classifier and ranking them according to their misclassification rate. A test was then done on the 10 best individually performing features where they were grouped in all possible combinations of 5 features to determine the feature combinations leading to the lowest error rates in a multi feature classifier. From this test 5 features were eventually chosen to do the classification. It was found that the features related to the signal energy and the spectral peaks in the 3Hz range gave the lowest errors. Error rates as low as 4% and 9% were achieved from a 5-feature linear classifier for the two data sets. The error rates from a 5-feature Neural Network classifier were found to be 6% and 12% respectively for these two data sets

Kinematická analýza rytmických pohybů: aplikace na třes rukou člověka a kmit křídel mušky octomilky.

Rytmický pohyb, pravidelný nebo nepravidelný, je nedílnou součástí motorického chování a to jak ve zdraví, tak v průběhu nemoci. Hlubší pochopení geneze rytmického pohybu je důležité pro porozumění patofyziologii onemocnění, mezi jejichž projevy rytmický pohyb patří. V disertační práci jsem studovala dva konkrétní aspekty rytmického pohybu: bilaterální koordinaci a modulární řízení. První z nich jsem analyzovala na třesu lidských rukou, druhý na pohybu křídel u modelového organismu Drosophila melanogaster (octomilka obecná). Mnoho typů třesu, včetně fyziologického třesu (PT) a esenciálního tremoru (ET), se vyskytuje v končetinách po obou stranách těla, s podobnou základní frekvencí kmitání. To naznačuje, že kontralaterální třesy mohou mít společný zdroj nebo jsou jinak spojené. Ve své studii jsem prozkoumala vazbu mezi třesem levé a pravé ruky. Pomocí 3D- akcelerometrů jsem změřila časový průběh třesu, a použila stacionární i nestacionární (waveletové) výpočetní metody k vyhodnocení bilaterální koherence. Měření na všech třech prostorových osách umožnilo prozkoumat ucelenější sadu kinematických proměnných, než ve většině předešlých studií. Nestacionární analýza usnadnila identifikaci časově transientní koherence, což je scénář, který se v analýze třesu dříve nebral v úvahu. U většiny subjektů s PT...Rhythmic motions, regular or irregular, are an integral part of motor behavior both in health and in disease. Better understanding of its neural control mechanisms helps in developing methods for controlling the progression of diseases manifesting as rhythmic motions. I studied two specific aspects of rhythmic motions: bilateral coordination of hand tremors in human subjects and modular control of locomotion in invertebrates. Many types of tremors, including the physiological tremor (PT) and the essential tremor (ET) occur in limbs on both the sides of the body, with similar fundamental frequency of the oscillation. This raises the possibility that the contralateral tremors may have a common source or are otherwise coupled. However, while significant contralateral interaction is seen in these two types of tremors, only limited evidence of bilateral coherence has been shown in the previous literature. Therefore, in my study I explored the existence of a weak coupling between the left and right oscillators the may lead to intermittent bilateral coherence. I measured triaxial acceleration of the two hands and systematically assessed their bilateral coherence, using both stationary and non-stationary (wavelet-based) analyses methods. Measuring all three axes allowed examination of a more complete set...Institute of Biophysics and Informatics First Faculty of Medicine Charles University in PragueÚstav biofyziky a informatiky 1. LF UK v PrazeFirst Faculty of Medicine1. lékařská fakult