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Most computer interfaces are designed in a one-size-fits-all fashion, which does not account for individual differences in abilities and preferences. Some computer users thrive with one software application while another user may struggle to use the same software. Some people tend to perform very well amidst distraction whereas others have a difficult time concentrating on a primary task when distracting agents are present. Much work has been done in quantifying a person's performance, but it has typically been difficult to quantify how difficult a task was for a person to perform. This thesis looks into the stress exhibited by various computer users while performing tasks in both their preferred and non-preferred modalities. The paper surveys the current physiological methods for analyzing human stress and delineates the hardware and software design and implementation of some of these methods. The physiological data-collecting hardware and software were deployed to collect physiological samples from test subjects engaging in memorization and recollection tasks in both an undistracted and a distracted setting. An analysis of the data shows the correlation between preferred modality and performance of tasks in that modality and other modalities. This analysis also shows the correlation between user arousal level and performance with and without distraction. Individual differences are considered by normalizing the physiological data collected for each subject prior to comparison with other subjects. The work presented herein gives insight into the individual differences of various types of computer users and is a precursor to work in adaptive user interface technology. / Master of Science

La motivation incite les apprenants à s’engager dans une activité et à persévérer dans son accomplissement afin d’atteindre un but. Dans les Systèmes Tutoriels Intelligents (STI), les études sur la motivation des apprenants possèdent trois manques importants : un manque de moyens objectifs et fiables pour évaluer cet état, un manque d’évaluation de rôles joués par les facteurs motivationnels conçus dans l’environnement d’apprentissage et un manque de stratégies d’interventions motivationnelles pour soutenir la motivation des apprenants. Dans cette thèse, nous nous intéressons à mieux comprendre l’état de la motivation des apprenant ainsi que les facteurs et stratégies motivationnels dans un environnement d’apprentissage captivant : les jeux sérieux. Dans une première étude, nous évaluons la motivation des apprenants par l’entremise d’un modèle théorique de la motivation (ARCS de Keller) et de données électro-physiologiques (la conductivité de la peau, le rythme cardiaque et l’activité cérébrale). Nous déterminons et évaluons aussi quelques situations ou stratégies favorisant la motivation dans l’environnement des jeux sérieux étudié. Dans une deuxième étude, nous développons un prototype de jeux sérieux intégrant – dans une première version – quelques éléments motivationnels issus de jeux vidéo et – dans une deuxième version – des stratégies motivationnelles d’un modèle théorique de la motivation. Nous espérons, avec une évaluation motivationnelle de notre prototype, soutenir les apprenants à atteindre des hauts niveaux de motivation, de persévérance et de performance. / Motivation encourages learners to be engaged in an activity and to persevere in its accomplishment in order to achieve a goal. In Intelligent Tutoring Systems (ITS), different studies of learners’ motivation have showed three major lacks: a lack of objective and reliable means to assess this state, a lack of evaluation of the roles played by motivational factors developed by learning environments, and a lack of motivational interventions to support learners’ motivation. In this thesis, we are interested in understanding the state of motivation, as well as motivational factors and strategies in an exciting learning environment: serious games. First, we carry out an empirical study to assess learners’ motivation using Keller’s ARCS psychological model combined with electro-physiological recordings, namely skin conductance, heart rate, and brain activity. We also identify and evaluate different situations and strategies that promote motivation in a serious game environment. Second, we develop a serious game which has some motivational elements (version 1) as well as different motivational strategies (version 2). Our serious game intends to support learners to rich high levels of motivation, perseverance and performance. We conduct an empirical assessment of learners’ motivation during interaction with our serious game.

La motivation incite les apprenants à s’engager dans une activité et à persévérer dans son accomplissement afin d’atteindre un but. Dans les Systèmes Tutoriels Intelligents (STI), les études sur la motivation des apprenants possèdent trois manques importants : un manque de moyens objectifs et fiables pour évaluer cet état, un manque d’évaluation de rôles joués par les facteurs motivationnels conçus dans l’environnement d’apprentissage et un manque de stratégies d’interventions motivationnelles pour soutenir la motivation des apprenants. Dans cette thèse, nous nous intéressons à mieux comprendre l’état de la motivation des apprenant ainsi que les facteurs et stratégies motivationnels dans un environnement d’apprentissage captivant : les jeux sérieux. Dans une première étude, nous évaluons la motivation des apprenants par l’entremise d’un modèle théorique de la motivation (ARCS de Keller) et de données électro-physiologiques (la conductivité de la peau, le rythme cardiaque et l’activité cérébrale). Nous déterminons et évaluons aussi quelques situations ou stratégies favorisant la motivation dans l’environnement des jeux sérieux étudié. Dans une deuxième étude, nous développons un prototype de jeux sérieux intégrant – dans une première version – quelques éléments motivationnels issus de jeux vidéo et – dans une deuxième version – des stratégies motivationnelles d’un modèle théorique de la motivation. Nous espérons, avec une évaluation motivationnelle de notre prototype, soutenir les apprenants à atteindre des hauts niveaux de motivation, de persévérance et de performance. / Motivation encourages learners to be engaged in an activity and to persevere in its accomplishment in order to achieve a goal. In Intelligent Tutoring Systems (ITS), different studies of learners’ motivation have showed three major lacks: a lack of objective and reliable means to assess this state, a lack of evaluation of the roles played by motivational factors developed by learning environments, and a lack of motivational interventions to support learners’ motivation. In this thesis, we are interested in understanding the state of motivation, as well as motivational factors and strategies in an exciting learning environment: serious games. First, we carry out an empirical study to assess learners’ motivation using Keller’s ARCS psychological model combined with electro-physiological recordings, namely skin conductance, heart rate, and brain activity. We also identify and evaluate different situations and strategies that promote motivation in a serious game environment. Second, we develop a serious game which has some motivational elements (version 1) as well as different motivational strategies (version 2). Our serious game intends to support learners to rich high levels of motivation, perseverance and performance. We conduct an empirical assessment of learners’ motivation during interaction with our serious game.

Sepsis is a life-threatening condition that neonatal patients are especially susceptible to. Fortunately, improved bedside monitoring has enabled the collection and use of continuous vital signs data for the purpose of detecting conditions such as sepsis. While current research has found some success in reducing mortality in neonatal intensive care units with linear directly interpretable models, such as logistic regression, accurate detection of sepsis from inherently noisy time-series data still remains a challenge. Furthermore, previous research has generally relied on pre-defined features extracted from rawvital signs data, which may not be optimal for the detection task. Therefore, assessing the overall feasibility of sequential deep learning models, such as recurrent and convolutional models, could improve the results of current research. This task was tackled in three phases. Firstly, baseline scores were established with a logistic regression model. Secondly, three common recurrent classifiers were tested on pre-defined window based features and compared with each other. Thirdly, a convolutional architecture with a recurrent and non-recurrent classifier was tested on raw low frequency (1Hz) signals in order to examine their capability to automatically extract features from the data. The final results from all phases were compared with each other. Results show that recurrent classifiers trained on pre-defined features do outperform automatic feature extraction with the convolutional models. The best model was based on a long-short term memory unit that achieved an area under the characteristic receiver operating unit curve of 0.806, and outperformed the established baseline results. In comparison with previous research, said model performed on par with the examined simple interpretable baseline models. The low results can likely be attributed to a insufficient sample size of patients with sepsis for the examined models and sub-optimal hyperparameter optimization due to the number of possible configurations. Further avenues of research include examination of high frequency data and more complex models for automatic feature extraction. / Sepsis är ett livshotande tillstånd som neonatala patienter är särskilt mottagliga för. Lyckligtvis har förbättrad patientmonitorering möjliggjort kontinuerlig insamling och andvänding av vitalparametrar i syfte att upptäcka tillstånd som sepsis. Medan aktuell forskning har funnit viss framgång i att minska dödligheten på neonatala intensivvårdsavdelningar med hjälp av linjära tolkbara modeller, såsom logistisk regression, är noggrann detektering av sepsis från brusig tidsseriedata fortfarande en utmaning. Dessutom har tidigare forskning i allmänhet förlitat sig på fördefinierade prediktorer extraherade från rå vitalparameterdata, som kanske inte är optimala för detektionsuppgiften. På grund av detta kan en bedömning av den övergripande användbarheten av sekventiella modeller för djupinlärning, såsom RNN- och CNN-modeller, förbättra resultaten av aktuell forskning. Denna uppgift tacklades i tre faser. Först och främst etablerades baslinjeresultat med en logistisk regressionsmodell. För det andra testades tre RNN-baserad klassificerare på data med fördefinierade fönsterbaserade prediktorer och jämfördes med varandra. För det tredje testades en CNN-arkitektur med både en RNN-klassificerare och MLP-klassificerare på råa lågfrekventa (1Hz) signaler för att undersöka deras förmåga att automatiskt extrahera egna prediktorer från datan. Slutresultaten från alla faser jämfördes med varandra. Resultaten visar att RNN-klassificerare som tränats på fördefinierade prediktorer överträffar automatisk extraktion av prediktorer med CNN-modellerna. Den bäst presterande modellen baserades på en långtidsminnesenhet som uppnådde en AUROC på 0.806, och överträffade de etablerade baslinjeresultaten. I jämförelse med tidigare forskning uppnådde ifrågavarande modell lika hög prestation som de väl undersökta enklare tolkbara baslinjemodellerna. De låga resultaten kan sannolikt tillskrivas en otillräcklig provstorlek av patienter med sepsis för de undersökta modellerna och suboptimal hyperparameteroptimering på grund av antalet möjliga konfigurationer. Ytterligare forskningsvägar inkluderar undersökning av högfrekventa data och mer komplexa modeller för automatisk extraktion av prediktorer.

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This thesis is an investigation of the complex processes and relationships between the physical human performer and the technologically manufactured cyber-human counterpart. I acted as both researcher and the physical human performer, deeply engaged in the moment-to-moment creation of events unfolding within a shared virtual reality environment. As the primary instigator and activator of the cyber-human partner, I maintained a balance between the live and technological performance elements, prioritizing the production of content and meaning. By way of using practice as research, this thesis argues that in considering interactions between cyber-human and human performers, it is crucial to move beyond discussions of technology when considering interactions between cyber-humans and human performers to an analysis of emotional content, the powers of poetic imagery, the trust that is developed through sensory perception and the evocation of complex relationships. A theoretical model is constructed to describe the relationship between a cyber-human and a human performer in the five works created specifically for this thesis, which is not substantially different from that between human performers. Technological exploration allows for the observation and analysis of various relationships, furthering an expanded understanding of ‘movement as content’ beyond the electronic connection. Each of the works created for this research used new and innovative technologies, including virtual reality, multiple interactive systems, six generations of wearable computers, motion capture technology, high-end digital lighting projectors, various projection screens, smart electronically charged fabrics, multiple sensory sensitive devices and intelligent sensory charged alternative performance spaces. They were most often collaboratively created in order to augment all aspects of the performance and create the sense of community found in digital live dance performances/events. These works are identified as one continuous line of energy and discovery, each representing a slight variation on the premise that a working, caring, visceral and poetic content occurs beyond the technological tools. Consequently, a shift in the physical human’s psyche overwhelms the act of performance. Scholarship and reflection on the works have been integral to my creative process throughout. The goals of this thesis, the works created and the resulting methodologies are to investigate performance to heighten the multiple ways we experience and interact with the world. This maximizes connection and results in a highly interactive, improvisational, dynamic, non-linear, immediate, accessible, agential, reciprocal, emotional, visceral and transformative experience without boundaries between the virtual and physical for physical humans, cyborgs and cyber-humans alike.