Trötthet i trafiken är ett stort problem i samhället. Det är särskilt farligt att trött framföra tunga lastbilar i trafiken eftersom dessa fordon är stora och har ofta livsavgörande roller vid inblandning i trafikolyckor. För att angripa problemet har det i denna rapport studerats kring vilket sömnstadie som är lämpligast att vakna under, i syfte att vakna pigg och alert samt vilka typer av tekniker och metoder som är lämpliga för att portabelt kunna detektera Rapid-Eye-Movement. Tidigare arbeten och studier har gjorts som påvisar att uppväckning i REM-sömn är optimalt för att känna sig alert. De valda metoderna är baserade på varianter av väletablerade tekniker som används för identifiering av sömnsteg. Elektrookulografi används för att mäta ögonrörelser med hjälp av fyra elektroder som är placerade på huvudet. Kroppsrörelser upptäcks genom en accelerometer som fästs på armen. Pulsmätningar görs och används för att räkna ut pulsvariansen under sömnen. Målet är att skapa en prototyp som ska känna av när användaren är i REM-sömn och sedan väcka användaren. Detta arbete är uppdelat i två inbyggda system som görs mellan två olika examensarbeten. Resultatet blev tre sensorer som fungerar individuellt. På grund av tidsbrist och en längre felsökning blev prototypen inte färdigställd. Innan sensorerna kan tillämpas i en produkt krävs det att ytterligare tester genomförs under monitorering av en sömnspecialist. / Tiredness in traffic is a major problem in society. It is especially dangerous to drive heavy trucks when tired because these vehicles are large and often have vital roles when involved in traffic accidents. To address the problem, this degree project has studied which sleep stage is most appropriate to wake up during, in order to wake up sharp and alert, and what types of techniques and methods are suitable for portable detection of Rapid-Eye-Movement. Previous work and studies have been done which indicates that awakening during REM sleep is optimal for feeling alert. The chosen methods are based on variants of well-established techniques that are used to identify sleep stages. Electrooculography is used to measure eye movements using four electrodes placed on the head. Body movements are detected by an accelerometer attached to the arm. Pulse measurements are made and used to calculate the pulse variation during sleep. The goal is to create a prototype which will know when the user is in REM sleep and then wake the user up. This work is divided into two embedded systems that are made between two different degree projects. The result was three sensors that worked individually. Due to lack of time and a longer troubleshooting, the prototype was not completed. Before the sensors can be used in a product, additional tests are required under the supervision of a sleep specialist.

L'hypovigilance correspond à la transition entre la veille et le sommeil durant laquelle l'organisme voit ses facultés d'observation et d'analyse fortement réduites. Elle est responsable de nombreux accidents sur la route. Le but de cette thèse est de proposer un système de détection de cette phase accidentogène à partir de l'analyse conjointe de l'activité cérébrale (électroencéphalogramme ou EEG) et d'une vidéo du conducteur. Dans un premier temps, une méthode non-supervisée de détection d'hypovigilance fonctionnant à l'aide d'un unique canal EEG a été proposée. Cette méthode, qui met en oeuvre différentes techniques de traitement du signal et de diagnostic, obtient de bonnes performances sur un ensemble de conducteurs, sans qu'il soit nécessaire de régler de paramètres. Dans un deuxième temps, nous nous sommes intéressés à la caractérisation des signes visuels de l'hypovigilance par une analyse vidéo des clignements. Une comparaison entre l'approche vidéo développée et l'approche traditionnelle par électro-oculogramme (EOG) a permis d'étudier dans quelle mesure la vidéo peut remplacer l'EOG pour la caractérisation des clignements. Elle a également permis de souligner la nécessité d'utiliser d'une caméra rapide (pouvant aller jusqu'à 200fps) pour caractériser les clignements. Un algorithme de détection d'hypovigilance à partir de la caractérisation vidéo des clignements a ainsi été développé. Pour finir, un algorithme de détection d'hypovigilance fusionnant, à l'aide de logique floue, les informations obtenues par les approches physiologique et vidéo est présenté. Toutes ces méthodes ont été testées et validées sur une base de données conséquente de conduite en état d'hypovigilance, la base de données ayant été expertisée par un spécialiste.

<p>Experiment One: The Effect of Noise on Autonomic Arousal</p><p><br></p><p>In response to the growing demand for research that helps us understand the complex interactions between Autonomic Arousal (AA) on behavior and performance there is an increasing need for robust techniques to efficiently utilize stimuli, such as sound, to vary the level of AA within a study. The goal of this study was to look at the impact of several factors, including sound intensity, order of presentation, and direction of presentation on skin conductance level, a widely utilized technique for approximating levels of AA. To do this we had 34 young adults ages 18- 34 listen to a series of 2-minute blocks of a sound stimuli based off a heating, ventilation, and air conditioning system (HVAC). Blocks included 5 single intensity conditions each block differing in 10 dBA steps ranging from 35-75 dBA. We presented blocks in both rising and falling level of intensity, with half the participants hearing them in a rising order first and half in a falling order first. The evidence found by this study suggests that increasing the sound level plays an important role in increasing AA and habituation is an extremely important factor that must be accounted for as it, in the case of typical young adults, quickly dampens the response to stimuli and subsequent stimuli. These findings suggest that researchers can best efficiently maximize the range of AA they can use while keeping their participants comfortable by starting out with the most intense stimuli and proceeding to the less intense stimuli, working with habitation instead of against it.</p><p> </p><p><br></p><p> Experiment Two: The Effect of Autonomic Arousal on Visual Attention</p><p><br></p><p>The goal of this study was to better understand how various levels of autonomic arousal impact different components of attentional control and if ASD-related traits indexed by Autism Quotient scores (AQ) might relate to alterations in this relationship. This study had 41 young adult participants (23 women, 17 men, 1 prefer not to say), ages ranging from 18 to 38 years old. Participants listened to varying levels of noise to induce changes in AA, which were recorded as changes in skin conductance level (SCL). To evaluate attentional control, participants preformed pro and anti-saccade visual gap–overlap paradigm tasks as measures of attentional control. The findings of this study suggest that increased levels of autonomic arousal are helpful for improving performance on anti-saccade tasks, which are heavily dependent on top-down attentional control. Additionally, increases in AQ scores were related to having less of a benefit from increasing levels of arousal on anti-saccade tasks. Additional interactions were also found and are discussed in this paper.</p>

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