Real-time performance modelling of a sustained attention to response task

Vigilance declines when exposed to highly predictable and uneventful tasks. Monotonous tasks provide little cognitive and motor stimulation and contribute to human errors. This paper aims to model and detect vigilance decline in real time through participant’s reaction times during a monotonous task. A lab-based experiment adapting the Sustained Attention to Response Task (SART) is conducted to quantify the effect of monotony on overall performance. Then relevant parameters are used to build a model detecting hypovigilance throughout the experiment. The accuracy of different mathematical models are compared to detect in real-time – minute by minute - the lapses in vigilance during the task. We show that monotonous tasks can lead to an average decline in performance of 45%. Furthermore, vigilance modelling enables to detect vigilance decline through reaction times with an accuracy of 72% and a 29% false alarm rate. Bayesian models are identified as a better model to detect lapses in vigilance as compared to Neural Networks and Generalised Linear Mixed Models. This modelling could be used as a framework to detect vigilance decline of any human performing monotonous tasks.

Predicting effects of monotony on driver’s vigilance

Monotony has been identified as a contributing factor to road crashes. Drivers’ ability to react to unpredictable events deteriorates when exposed to highly predictable and uneventful driving tasks, such as driving on Australian rural roads, many of which are monotonous by nature. Highway design in particular attempts to reduce the driver’s task to a merely lane-keeping one. Such a task provides little stimulation and is monotonous, thus affecting the driver’s attention which is no longer directed towards the road. Inattention contributes to crashes, especially for professional drivers. Monotony has been studied mainly from the endogenous perspective (for instance through sleep deprivation) without taking into account the influence of the task itself (repetitiveness) or the surrounding environment. The aim and novelty of this thesis is to develop a methodology (mathematical framework) able to predict driver lapses of vigilance under monotonous environments in real time, using endogenous and exogenous data collected from the driver, the vehicle and the environment. Existing approaches have tended to neglect the specificity of task monotony, leaving the question of the existence of a “monotonous state” unanswered. Furthermore the issue of detecting vigilance decrement before it occurs (predictions) has not been investigated in the literature, let alone in real time. A multidisciplinary approach is necessary to explain how vigilance evolves in monotonous conditions. Such an approach needs to draw on psychology, physiology, road safety, computer science and mathematics. The systemic approach proposed in this study is unique with its predictive dimension and allows us to define, in real time, the impacts of monotony on the driver’s ability to drive. Such methodology is based on mathematical models integrating data available in vehicles to the vigilance state of the driver during a monotonous driving task in various environments. The model integrates different data measuring driver’s endogenous and exogenous factors (related to the driver, the vehicle and the surrounding environment). Electroencephalography (EEG) is used to measure driver vigilance since it has been shown to be the most reliable and real time methodology to assess vigilance level. There are a variety of mathematical models suitable to provide a framework for predictions however, to find the most accurate model, a collection of mathematical models were trained in this thesis and the most reliable was found. The methodology developed in this research is first applied to a theoretically sound measure of sustained attention called Sustained Attention Response to Task (SART) as adapted by Michael (2010), Michael and Meuter (2006, 2007). This experiment induced impairments due to monotony during a vigilance task. Analyses performed in this thesis confirm and extend findings from Michael (2010) that monotony leads to an important vigilance impairment independent of fatigue. This thesis is also the first to show that monotony changes the dynamics of vigilance evolution and tends to create a “monotonous state” characterised by reduced vigilance. Personality traits such as being a low sensation seeker can mitigate this vigilance decrement. It is also evident that lapses in vigilance can be predicted accurately with Bayesian modelling and Neural Networks. This framework was then applied to the driving task by designing a simulated monotonous driving task. The design of such task requires multidisciplinary knowledge and involved psychologist Rebecca Michael. Monotony was varied through both the road design and the road environment variables. This experiment demonstrated that road monotony can lead to driving impairment. Particularly monotonous road scenery was shown to have the most impact compared to monotonous road design. Next, this study identified a variety of surrogate measures that are correlated with vigilance levels obtained from the EEG. Such vigilance states can be predicted with these surrogate measures. This means that vigilance decrement can be detected in a car without the use of an EEG device. Amongst the different mathematical models tested in this thesis, only Neural Networks predicted the vigilance levels accurately. The results of both these experiments provide valuable information about the methodology to predict vigilance decrement. Such an issue is quite complex and requires modelling that can adapt to highly inter-individual differences. Only Neural Networks proved accurate in both studies, suggesting that these models are the most likely to be accurate when used on real roads or for further research on vigilance modelling. This research provides a better understanding of the driving task under monotonous conditions. Results demonstrate that mathematical modelling can be used to determine the driver’s vigilance state when driving using surrogate measures identified during this study. This research has opened up avenues for future research and could result in the development of an in-vehicle device predicting driver vigilance decrement. Such a device could contribute to a reduction in crashes and therefore improve road safety.

The application of neural networks to induction machine control

Neural networks (NNs) are discussed in connection with their possible use in induction machine drives. The mathematical model of the NN as well as a commonly used learning algorithm is presented. Possible applications of NNs to induction machine control are discussed. A simulation of an NN successfully identifying the nonlinear multivariable model of an induction-machine stator transfer function is presented. Previously published applications are discussed, and some possible future applications are proposed.

Identification and control of induction machines using artificial neural networks

The use of artificial neural networks (ANNs) to identify and control induction machines is proposed. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics, and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Both systems are inherently adaptive as well as self-commissioning. The current controller is a completely general nonlinear controller which can be used together with any drive algorithm. Various advantages of these control schemes over conventional schemes are cited, and the combined speed and current control scheme is compared with the standard vector control scheme

Identification and control of induction machines using artificial neural networks

This paper proposes the use of artificial neural networks (ANNs) to identify and control an induction machine. Two systems are presented: a system to adaptively control the stator currents via identification of the electrical dynamics; and a system to adaptively control the rotor speed via identification of the mechanical and current-fed system dynamics. Various advantages of these control schemes over other conventional schemes are cited and the performance of the combined speed and current control scheme is compared with that of the standard vector control scheme

Alleviating ‘overfitting’ via genetically-regularised neural network

A hybrid genetic algorithm/scaled conjugate gradient regularisation method is designed to alleviate ANN `over-fitting'. In application to day-ahead load forecasting, the proposed algorithm performs better than early-stopping and Bayesian regularisation, showing promising initial results.

Biceps brachii muscle oxygenation in electrical muscle stimulation

The purpose of this study was to compare between electrical muscle stimulation (EMS) and maximal voluntary (VOL) isometric contractions of the elbow flexors for changes in biceps brachii muscle oxygenation (tissue oxygenation index, TOI) and haemodynamics (total haemoglobin volume, tHb = oxygenated-Hb + deoxygenated-Hb) determined by near-infrared spectroscopy (NIRS). The biceps brachii muscle of 10 healthy men (23–39 years) was electrically stimulated at high frequency (75 Hz) via surface electrodes to evoke 50 intermittent (4-s contraction, 15-s relaxation) isometric contractions at maximum tolerated current level (EMS session). The contralateral arm performed 50 intermittent (4-s contraction, 15-s relaxation) maximal voluntary isometric contractions (VOL session) in a counterbalanced order separated by 2–3 weeks. Results indicated that although the torque produced during EMS was approximately 50% of VOL (P<0Æ05), there was no significant difference in the changes in TOI amplitude or TOI slope between EMS and VOL over the 50 contractions. However, the TOI amplitude divided by peak torque was approximately 50% lower for EMS than VOL (P<0Æ05), which indicates EMS was less efficient than VOL. This seems likely because of the difference in the muscles involved in the force production between conditions. Mean decrease in tHb amplitude during the contraction phases was significantly (P<0Æ05) greater for EMS than VOL from the 10th contraction onwards, suggesting that the muscle blood volume was lower in EMS than VOL. It is concluded that local oxygen demand of the biceps brachii sampled by NIRS is similar between VOL and EMS.

Pedunculopontine nucleus deep brain stimulation produces sustained improvement in primary progressive freezing of gait

Objective: To assess the efficacy of bilateral pedunculopontine nucleus (PPN) deep brain stimulation (DBS) as a treatment for primary progressive freezing of gait (PPFG). ------ ----- Methods: A patient with PPFG underwent bilateral PPN-DBS and was followed clinically for over 14 months. ------ ----- Results: The PPFG patient exhibited a robust improvement in gait and posture following PPN-DBS. When PPN stimulation was deactivated, postural stability and gait skills declined to pre-DBS levels, and fluoro-2-deoxy-d-glucose positron emission tomography revealed hypoactive cerebellar and brainstem regions, which significantly normalised when PPN stimulation was reactivated. ------ ----- Conclusions: This case demonstrates that the advantages of PPN-DBS may not be limited to addressing freezing of gait (FOG) in idiopathic Parkinson's disease. The PPN may also be an effective DBS target to address other forms of central gait failure.

Biaxial cell stimulation : a mechanical validation

To analyse mechanotransduction resulting from tensile loading under defined conditions, various devices for in vitro cell stimulation have been developed. This work aimed to determine the strain distribution on the membrane of a commercially available device and its consistency with rising cycle numbers, as well as the amount of strain transferred to adherent cells. The strains and their behaviour within the stimulation device were determined using digital image correlation (DIC). The strain transferred to cells was measured on eGFP-transfected bone marrow-derived cells imaged with a fluorescence microscope. The analysis was performed by determining the coordinates of prominent positions on the cells, calculating vectors between the coordinates and their length changes with increasing applied tensile strain. The stimulation device was found to apply homogeneous (mean of standard deviations approx. 2% of mean strain) and reproducible strains in the central well area. However, on average, only half of the applied strain was transferred to the bone marrow-derived cells. Furthermore, the strain measured within the device increased significantly with an increasing number of cycles while the membrane's Young's modulus decreased, indicating permanent changes in the material during extended use. Thus, strain magnitudes do not match the system readout and results require careful interpretation, especially at high cycle numbers.

The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.

Neural network learning:theoretical foundations

This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics