13,122 research outputs found

    Computational Approaches for Monitoring of Health Parameters and Their Evaluation for Application in Clinical Setting.

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    The algorithms and mathematical methods developed in this work focus on using computational approaches for low cost solution of health care problems for better patient outcome. Furthermore, evaluation of those approaches for clinical application considering the risk and benefit in a clinical setting is studied. Those risks and benefits are discussed in terms of sensitivity, specificity and area under the receiver operating characteristics curve. With a rising cost of health care and increasing number of aging population, there is a need for innovative and low cost solutions for health care problems. In this work, algorithms, mathematical techniques for the solutions of the problems related to physiological parameter monitoring have been explored and their evaluation approaches for application in a clinical setting have been studied. The physiological parameters include affective state, pain level, heart rate, oxygen saturation, hemoglobin level and blood pressure. For the mathematical basis development for different data intensive problems, eigenvalue based methods along with others have been used in designing innovative solutions for health care problems, developing new algorithms for smart monitoring of patients; from home monitoring to combat casualty situations. Eigenvalue based methods already have wide applications in many areas such as analysis of stability in control systems, search algorithms (Google Page Rank), Eigenface methods for face recognition, principal component analysis for data compression and pattern recognition. Here, the research work in 1) multi-parameter monitoring of affective state, 2) creating a smart phone based pain detection tool from facial images, 3) early detection of hemorrhage from arterial blood pressure data, 4) noninvasive measurement of physiological signals including hemoglobin level and 5) evaluation of the results for clinical application are presented

    Mapping from Frame-Driven to Frame-Free Event-Driven Vision Systems by Low-Rate Rate-Coding and Coincidence Processing. Application to Feed-Forward ConvNets

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    Event-driven visual sensors have attracted interest from a number of different research communities. They provide visual information in quite a different way from conventional video systems consisting of sequences of still images rendered at a given “frame rate”. Event-driven vision sensors take inspiration from biology. Each pixel sends out an event (spike) when it senses something meaningful is happening, without any notion of a frame. A special type of Event-driven sensor is the so called Dynamic-Vision-Sensor (DVS) where each pixel computes relative changes of light, or “temporal contrast”. The sensor output consists of a continuous flow of pixel events which represent the moving objects in the scene. Pixel events become available with micro second delays with respect to “reality”. These events can be processed “as they flow” by a cascade of event (convolution) processors. As a result, input and output event flows are practically coincident in time, and objects can be recognized as soon as the sensor provides enough meaningful events. In this paper we present a methodology for mapping from a properly trained neural network in a conventional Frame-driven representation, to an Event-driven representation. The method is illustrated by studying Event-driven Convolutional Neural Networks (ConvNet) trained to recognize rotating human silhouettes or high speed poker card symbols. The Event-driven ConvNet is fed with recordings obtained from a real DVS camera. The Event-driven ConvNet is simulated with a dedicated Event-driven simulator, and consists of a number of Event-driven processing modules the characteristics of which are obtained from individually manufactured hardware modules

    Sleep-Related Arousal and Spontaneous Movement Properties in Methadone-Exposed Neonates: A Videographic Assessment On the First or Second Postnatal Night

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    Prenatal substance exposure such as alcohol, nicotine, and opiates is known to modulate autonomic regulatory function during sleep, and to decrease arousability and spontaneous movements (SM). SM during sleep may reflect a protective mechanism for immature patterns of arousals. Neurodevelopmental compromise in sleep and arousal systems may underlie sudden infant death syndrome (SIDS) risk in which infants expire during sleep. Previous studies from our laboratory found abnormal patterns of neonatal arousal, sleep fragmentation, and deficits in sleep-related SM in infants with prenatal alcohol exposure. In this study, prenatal exposure to methadone was hypothesized to disrupt the development of sleep and arousal neural circuitry, which have been found for other high-risk samples. Neonatal Abstinence Syndrome (NAS) is a common consequence of prenatal methadone exposure that may appear within 24 - 72 hours postbirth, and is known to disrupt sleep due to hyperarousability. As a secondary hypothesis, the neonatal age (day 1 or 2 of life) was expected to affect infant sleep and arousal outcomes in methadone-exposed neonates particularly on day 2 when NAS symptoms increase. Additionally, single nucleotide polymorphism (SNP) in the catechol-O-methyltransferase (COMT) gene was found to associate with the severity of NAS in our previous study. NAS severity has been associated with sleep disorders. Therefore, the second hypothesis of this thesis study is that the minor allelic variants (AG/GG) of the COMT gene previously identified as protective of NAS severity may also associate with better sleep organization and more robust SM than the carriers of the AA genotype. Rural, disadvantaged Caucasian mothers and infants (N=58 dyads: methadone=37, comparison=21) were recruited from multiple narcotic treatment sites and prenatal clinic at Eastern Maine Medical Center (EMMC). Mothers were interviewed to determine demographics, psychiatric status, and substance abuse history during the 3rd trimester. Biweekly maternal urinalysis screens and neonatal meconium were applied to verify comorbid alcohol, tobacco, and other drug use. Finnegan scores determined symptoms of withdrawal in opioid exposed newborns. Videosomnographic recordings of behavioral states were collected in the newborn nursery of EMMC overnight, and recordings between 2400-0500h were analyzed for frequency and duration of sleep, wake, arousal, and SM. Saliva samples for genetic study was collected using OrageneTM kits. Results from behavioral state analysis (n=50) showed that methadone-exposed neonates were significantly hyper-aroused and crying more on both day 1 and 2 of life (p\u3c.05); and both the frequency and duration of these parameters increased significantly in the methadoneexposed neonates on day 2 of life, as expected. In the genetic study (n=20), neonates with NAS protective AG/GG genotypes showed better behavioral sleep, fewer arousals, and robust SM than infants with NAS risk AA genotype (p\u3c.05). These findings support evidence of sleep fragmentation in the exposed neonates that is exacerbated by the passage of time since birth when withdrawal symptoms compound the intensity of sleep disturbance and infant distress. Consistent with other findings from other SIDS-risk samples, these findings indicate that arousal and SM regulation may be disrupted in methadone-exposed neonates, suggesting that prenatal methadone may increase risk for SIDS

    Affective games:a multimodal classification system

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    Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation

    The effect of parent interactions on young infants’ visual attention in an object manipulation task.

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    The Sticky Mittens (SM) task, an object-manipulation task that facilitates typically developing pre-reaching infants’ learning through active experience with objects, is often utilized to understand how experience affects young infants’ learning about objects. SM experience has been shown to increase infants’ attention to objects, object engagement, and object exploration (Libertus & Needham, 2010; Needham, Barrett, & Peterman, 2002) and facilitates development of causal perception (Rakison & Krogh, 2012; Holt, 2016). Although the majority of SM studies have involved parents interacting naturally with their infants, few have focused on how those interactions affect infants’ learning and performance during or after SM. Holt (2016) found that infants in an active, no parent encouragement condition (AN) exhibited causal perception following a brief in-lab SM training session, while infants in an active, parent encouragement condition (AE) did not. I hypothesized that parent interaction behaviors in the AE condition disrupted infants’ attention to objects and may have negatively impacted infants’ learning. In the present study, videos from Holt’s (2016) AE and AN conditions were coded to compare the effect of parent interactions on infant attention to objects across conditions. While no significant effects were found on overall measures of infant attention or parent interactions, infants in the AE condition were more likely to look away from the toys following a parent interaction than were infants in the AN condition, supporting the hypothesis that parents in the encouragement condition distracted their infants during SM training. These findings are an important first step in understanding the role of parent interactions in the SM literature, infant attention, and infant attention to objects and learning
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