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BIOSIGNAL PROCESSING CHALLENGES IN EMOTION RECOGNITIONFOR ADAPTIVE LEARNING
Title
BIOSIGNAL PROCESSING CHALLENGES IN EMOTION RECOGNITIONFOR ADAPTIVE LEARNING.
Creator
Vartak, Aniket, Mikhael, Wasfy, University of Central Florida
Abstract / Description
User-centered computer based learning is an emerging field of interdisciplinary research. Research in diverse areas such as psychology, computer science, neuroscience and signal processing is making contributions the promise to take this field to the next level. Learning systems built using contributions from these fields could be used in actual training and education instead of just laboratory proof-of-concept. One of the important advances in this research is the detection and assessment of...
Show moreUser-centered computer based learning is an emerging field of interdisciplinary research. Research in diverse areas such as psychology, computer science, neuroscience and signal processing is making contributions the promise to take this field to the next level. Learning systems built using contributions from these fields could be used in actual training and education instead of just laboratory proof-of-concept. One of the important advances in this research is the detection and assessment of the cognitive and emotional state of the learner using such systems. This capability moves development beyond the use of traditional user performance metrics to include system intelligence measures that are based on current neuroscience theories. These advances are of paramount importance in the success and wide spread use of learning systems that are automated and intelligent. Emotion is considered an important aspect of how learning occurs, and yet estimating it and making adaptive adjustments are not part of most learning systems. In this research we focus on one specific aspect of constructing an adaptive and intelligent learning system, that is, estimation of the emotion of the learner as he/she is using the automated training system. The challenge starts with the definition of the emotion and the utility of it in human life. The next challenge is to measure the co-varying factors of the emotions in a non-invasive way, and find consistent features from these measures that are valid across wide population. In this research we use four physiological sensors that are non-invasive, and establish a methodology of utilizing the data from these sensors using different signal processing tools. A validated set of visual stimuli used worldwide in the research of emotion and attention, called International Affective Picture System (IAPS), is used. A dataset is collected from the sensors in an experiment designed to elicit emotions from these validated visual stimuli. We describe a novel wavelet method to calculate hemispheric asymmetry metric using electroencephalography data. This method is tested against typically used power spectral density method. We show overall improvement in accuracy in classifying specific emotions using the novel method. We also show distinctions between different discrete emotions from the autonomic nervous system activity using electrocardiography, electrodermal activity and pupil diameter changes. Findings from different features from these sensors are used to give guidelines to use each of the individual sensors in the adaptive learning environment.
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Date Issued
2010
Identifier
CFE0003301, ucf:48503
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0003301
Brain stethoscope: A non-invasive method for monitoring intracranial pressure
Title
Brain stethoscope: A non-invasive method for monitoring intracranial pressure.
Creator
Azad, Md Khurshidul, Mansy, Hansen, Kassab, Alain, Bhattacharya, Samik, University of Central Florida
Abstract / Description
Monitoring intracranial pressure (ICP) is important for patients with increased intracranial pressure. Invasive methods of ICP monitoring include lumbar puncture manometry, which requires high precision, is costly, and can lead to complications. Non-invasive monitoring of ICP using tympanic membrane pulse (TMp) measurement can provide an alternative monitoring method that avoids such complications. In the current study, a piezo based sensor was designed, constructed and used to acquire TMp...
Show moreMonitoring intracranial pressure (ICP) is important for patients with increased intracranial pressure. Invasive methods of ICP monitoring include lumbar puncture manometry, which requires high precision, is costly, and can lead to complications. Non-invasive monitoring of ICP using tympanic membrane pulse (TMp) measurement can provide an alternative monitoring method that avoids such complications. In the current study, a piezo based sensor was designed, constructed and used to acquire TMp signals. The results showed that tympanic membrane waveform changed in morphology and amplitude with increased ICP, which was induced by changing subject position using a tilt table. In addition, the results suggest that TMp are affected by breathing, which has small effects on ICP. The newly developed piezo based brain stethoscope may be a way to monitor patients with increased intracranial pressure thus avoiding invasive ICP monitoring and reducing associated risk and cost.
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Date Issued
2018
Identifier
CFE0006972, ucf:51643
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006972
SIGNAL PROCESSING OF AN ECG SIGNALIN THE PRESENCE OF A STRONG STATIC MAGNETIC FIELD
Title
SIGNAL PROCESSING OF AN ECG SIGNALIN THE PRESENCE OF A STRONG STATIC MAGNETIC FIELD.
Creator
Gupta, Aditya, Weeks, Arthur, University of Central Florida
Abstract / Description
This dissertation addresses the problem of elevation of the T wave of an electrocardiogram (ECG) signal in the magnetic resonance imaging (MRI). In the MRI, due to the strong static magnetic field the interaction of the blood flow with this strong magnetic field induces a voltage in the body. This voltage appears as a superimposition at the locus of the T wave of the ECG signal. This looses important information required by the doctors to interpret the ST segment of the ECG and detect...
Show moreThis dissertation addresses the problem of elevation of the T wave of an electrocardiogram (ECG) signal in the magnetic resonance imaging (MRI). In the MRI, due to the strong static magnetic field the interaction of the blood flow with this strong magnetic field induces a voltage in the body. This voltage appears as a superimposition at the locus of the T wave of the ECG signal. This looses important information required by the doctors to interpret the ST segment of the ECG and detect diseases such as myocardial infarction. This dissertation aims at finding a solution to the problem of elevation of the T wave of an ECG signal in the MRI. The first step is to simulate the entire situation and obtain the magnetic field dependent T wave elevation. This is achieved by building a model of the aorta and simulating the blood flow in it. This model is then subjected to a static magnetic field and the surface potential on the thorax is measured to observe the T wave elevation. The various parameters on which the T wave elevation is dependent are then analyzed. Different approaches are used to reduce this T wave elevation problem. The direct approach aims at computing the magnitude of T wave elevation using magneto-hydro-dynamic equations. The indirect approach uses digital signal processing tools like the least mean square adaptive filter to remove the T wave elevation and obtain artifact free ECG signal in the MRI. Excellent results are obtained from the simulation model. The model perfectly simulates the ECG signal in the MRI at all the 12 leads of the ECG. These results are compared with ECG signals measured in the MRI. A simulation package is developed in MATLAB based on the simulation model. This package is a graphical user interface allowing the user to change the strength of magnetic field, the radius of the aorta and the orientation of the aorta with respect to the heart and observe the ECG signals with the elevation at the 12 leads of the ECG. Also the artifacts introduced due to the magnetic field can be removed by the least mean square adaptive filter. The filter adapts the ECG signal in the MRI to the ECG signal of the patient outside the MRI. Before the adaptation, the heart rate of the ECG outside the MRI is matched to the ECG in the MRI by interpolation or decimation. The adaptive filter works excellently to remove the T wave artifacts. When the cardiac output of the patient changes, the simulation model is used along with the adaptive filter to obtain the artifact free ECG signal.
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Date Issued
2007
Identifier
CFE0001857, ucf:47389
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0001857