Today many strategies of monitoring health status and well-being are done through measurementmethods that are connected to the body, e.g. sensors or electrodes. These are often complicatedand requires personal assistance in order to use, because of advanced hardware and attachmentissues. This paper proposes a new method of making it possible for a user to self-monitoring theirwell-being and health status over time by using a non-contact camera system. The camera systemextracts physiological parameters (e.g. Heart Rate (HR), Respiration Rate (RR), Inter-bit-Interval(IBI)) based on facial color variations, due to blood circulation in facial skin. By examining anindividual’s physiological parameters, one can extract measurements that can be used in order tomonitor their well-being. The measurements used in this paper is features of heart rate variability(HRV) that are calculated from the physiological parameter IBI. The HRV features included andtested in this paper is SDNN, RMSSD, NN50 and pNN50 from Time Domain and VLF, LF andLF/HF from Frequency Domain. Machine Learning classification is done in order to classifyan individual’s sleepiness from the given features. The Machine Learning classification modelwhich gave the best results, in forms of accuracy, were Support Vector Machines (SVM). The bestmean accuracy achieved was 84,16% for the training set and 81,67% for the test set for sleepinessdetection with SVM. This paper has great potential for personal health care monitoring and can befurther extended to detect other factors that could help a user to monitor their well-being, such asmeasuring stress level

<p>In this master thesis project quantitative testing in a truck simulator with 22 participants were conducted during which ten in-vehicle variables were measured. Examples of measured variables are steering wheel torque, lateral position and yaw angle. These measured variables were then used to calculate 17 independent variables that all to some extent explain the sleepiness level of the driver. The drivers’ sleepiness level was measured using the Karolinska Sleepiness Scale (KSS) in order to judge the performance of the independent variables. The combination of the 17 independent variables that best explain the sleepiness level of the driver is then extracted using multiple regression analysis with forward selection.</p><p>Sometimes some of the independent variables are not defined; therefore different models were created to handle all possible combinations of valid and invalid independent variables. The final system uses six different models to predict the sleepiness level of the driver.</p><p>The performance of the final system showed promising results. The system can correctly classify the drivers in approximately 87% of the cases. The number of occasions when the system classify the driver as sleepy when he/she is still alert is very low, approximately 0.7%.</p>

In this master thesis project quantitative testing in a truck simulator with 22 participants were conducted during which ten in-vehicle variables were measured. Examples of measured variables are steering wheel torque, lateral position and yaw angle. These measured variables were then used to calculate 17 independent variables that all to some extent explain the sleepiness level of the driver. The drivers’ sleepiness level was measured using the Karolinska Sleepiness Scale (KSS) in order to judge the performance of the independent variables. The combination of the 17 independent variables that best explain the sleepiness level of the driver is then extracted using multiple regression analysis with forward selection. Sometimes some of the independent variables are not defined; therefore different models were created to handle all possible combinations of valid and invalid independent variables. The final system uses six different models to predict the sleepiness level of the driver. The performance of the final system showed promising results. The system can correctly classify the drivers in approximately 87% of the cases. The number of occasions when the system classify the driver as sleepy when he/she is still alert is very low, approximately 0.7%.

The cost of sleepiness-related accidents runs into tens of billions of dollars per year in America alone (Leger, 1994), and can play a contributing role in motor vehicle accidents and large-scale industrial disasters (Reason, 1990). Likewise, the effects of an ill-timed distraction or otherwise lack of attention to a main task can be the difference between elevated risk, or simply a lack of productivity. The interaction between sleepiness and distraction is poorly researched, and little is known about the mechanisms and scale of the problems associated by this interaction. Therefore, we sought to determine the effects of sleepiness and distraction using overnight and daytime sleepiness with various levels of distraction on three tasks ranging from a simple vigilance task to a challenging luggage x-ray inspection task. The first and second studies examined overnight sleepiness (7pm to 7am) for twenty-four healthy participants (m = 23.2yrs old - same for both studies) using a psychomotor task compared to a systems monitoring task, while also manipulating peripheral distraction through a television playing a comedy series. The results showed significant effects of sleepiness on the psychomotor task and evidence for interactive effects of distraction, whereas the systems monitoring task showed no changes with either sleepiness or distraction. Subjects were far more prone to distraction when sleepy for both tasks, and EEG findings suggest that the alpha frequency (8-13Hz) power increases reflect impairments of performance. There is a decaying . exponential relationship between the probability of a subject's eyes being open as the response time increases, such that longer responses above three seconds are 95% likely to have occurred with the eyes closed. The third study used a sample of twelve young (m = 20.8yrs) and twelve older (m = 60.0yrs) participants, and examined the effects of sleep restriction (< 5hrs vs normal sleep) with three levels of distraction (no distraction, peripheral in the form of television and cognitive distraction as a simulated conversation by means of verbal fluency task). The task used was an x-ray luggage search simulator that is functionally similar to the task used for airport security screening. The practice day showed that speed and accuracy on the task improved with successive sessions, but that the older group were markedly slower and less accurate than the younger group even before the experimental manipulations. There was no effect of daytime sleep restriction for either the younger or older groups between the two experimental days. However, distraction was found to impair the performance of both young and old, with the cognitive distraction proving to be the most difficult condition. Overall, it is concluded that overnight sleepiness impairs performance in monotonous tasks, but these risks can be diminished by making tasks more engaging. Distractions can affect performance, but may be difficult to quantify as subjects create strategies that allow themselves to attend to distractions during the undemanding moments of a task. Continuous cognitive distraction does affect performance, particularly in older subjects, who are less able to manage concurrent demands effectively. Humans appear capable of coping Sleepiness and Distraction iv with a 40% loss of their usual sleep quota or 24-hours of sleep restriction on complex tasks, but performance degrades markedly on monotonous tasks. Performances for simple and complex tasks are impaired by distracters when the effect of distraction is large enough, but the magnitude of impairment depends on how challenging the task is or how well the subject is able to cope with the distractions.

É crescente a preocupação com as doenças do sono e o seu impacto na saúde da população em geral. Pouco ainda é conhecido e explorado na região Sul de Santa Catarina. Embora a literatura reforce a necessidade de uma avaliação objetiva, existem bons instrumentos clínicos que podem ser utilizados em estudos epidemiológicos. Foi realizado um estudo transversal e observacional com o objetivo de estimar a prevalência de sintomas relacionados a apneia obstrutiva do sono em um grupo de pacientes atendidos no Ambulatório de Clínicas Integradas da Universidade do Extremo Sul Catarinense, um ambulatório geral secundário, na cidade de Criciúma. Foram avaliados 101 pacientes através da aplicação de questões de sintomas de apneia validadas pelo estudo PLATINO e da Escala de Sonolência de Epworth. A amostra estudada tinha 73 (72,3%) mulheres; 47 (48,6%) indivíduos estudados tinham entre 40 e 59 anos. O ronco esteve presente em 65 (64,4%) pacientes e 20 (19,8%) relataram apneias. Dos indivíduos entrevistados, 31 (30,7%) relataram já terem sido questionados pelo seu médico a respeito do seu sono. Quanto ao resultado da ESE, 14 (13,9%) tiveram pontuação superior a 10. No sexo masculino, foi encontrada uma relação entre a presença de ronco e apneia e escores na ESE acima de 10 (p< 0,01). Concluímos que em uma população não selecionada o ronco foi um sintoma bastante prevalente, assim como o relato de apneias. As questões referentes ao sono, sobretudo relacionadas à apneia do sono, ainda são pouco abordadas pelos médicos em consultas gerais. / This observational cross-sectional study was conducted to determine the prevalence of symptoms related to obstructive sleep apnea in a group of patients attending Clínicas Integradas da Universidade do Extremo Sul Catarinense Ambulatory, a secondary ambulatory, in Criciuma, Brazil. Inclusion criteria was age 18 years or older. 101 patients were interviewed, and it were apllied questions related to obstructive apnea symptoms, previously validated in the PLATINO study and Epworth Sleepiness Scale. The sample was compounded by 73 (72,3%) women. 47 (48,6%) individuals were between 40 and 59 years old. Snores were present in 65 (64,4%) patients and 20 (19,8%) referred witnessed apneas during sleep. Thirty one (30,7%)of the participants said their physician have ever asked about sleep. The result on Epworth Sleepiness Scale was greater than 10 in 14 (13,9%) individuals. There was a significant relation between snore, witnessed apneas and Epworth Sleepiness Scale results greater than 10 in males (p<0,01). The prevalence of obstructive sleep apnea symptoms are high in this population, but Epworth Sleepiness Scale values are low. Besides, physicians still don’t ask much about sleep.

É crescente a preocupação com as doenças do sono e o seu impacto na saúde da população em geral. Pouco ainda é conhecido e explorado na região Sul de Santa Catarina. Embora a literatura reforce a necessidade de uma avaliação objetiva, existem bons instrumentos clínicos que podem ser utilizados em estudos epidemiológicos. Foi realizado um estudo transversal e observacional com o objetivo de estimar a prevalência de sintomas relacionados a apneia obstrutiva do sono em um grupo de pacientes atendidos no Ambulatório de Clínicas Integradas da Universidade do Extremo Sul Catarinense, um ambulatório geral secundário, na cidade de Criciúma. Foram avaliados 101 pacientes através da aplicação de questões de sintomas de apneia validadas pelo estudo PLATINO e da Escala de Sonolência de Epworth. A amostra estudada tinha 73 (72,3%) mulheres; 47 (48,6%) indivíduos estudados tinham entre 40 e 59 anos. O ronco esteve presente em 65 (64,4%) pacientes e 20 (19,8%) relataram apneias. Dos indivíduos entrevistados, 31 (30,7%) relataram já terem sido questionados pelo seu médico a respeito do seu sono. Quanto ao resultado da ESE, 14 (13,9%) tiveram pontuação superior a 10. No sexo masculino, foi encontrada uma relação entre a presença de ronco e apneia e escores na ESE acima de 10 (p< 0,01). Concluímos que em uma população não selecionada o ronco foi um sintoma bastante prevalente, assim como o relato de apneias. As questões referentes ao sono, sobretudo relacionadas à apneia do sono, ainda são pouco abordadas pelos médicos em consultas gerais. / This observational cross-sectional study was conducted to determine the prevalence of symptoms related to obstructive sleep apnea in a group of patients attending Clínicas Integradas da Universidade do Extremo Sul Catarinense Ambulatory, a secondary ambulatory, in Criciuma, Brazil. Inclusion criteria was age 18 years or older. 101 patients were interviewed, and it were apllied questions related to obstructive apnea symptoms, previously validated in the PLATINO study and Epworth Sleepiness Scale. The sample was compounded by 73 (72,3%) women. 47 (48,6%) individuals were between 40 and 59 years old. Snores were present in 65 (64,4%) patients and 20 (19,8%) referred witnessed apneas during sleep. Thirty one (30,7%)of the participants said their physician have ever asked about sleep. The result on Epworth Sleepiness Scale was greater than 10 in 14 (13,9%) individuals. There was a significant relation between snore, witnessed apneas and Epworth Sleepiness Scale results greater than 10 in males (p<0,01). The prevalence of obstructive sleep apnea symptoms are high in this population, but Epworth Sleepiness Scale values are low. Besides, physicians still don’t ask much about sleep.

No description available.

Driver sleepiness is known to be a major contributor to road traffic incidents (RTIs). An initial literature review identified many studies reporting untreated obstructive sleep apnoea (OSA) sufferers as having impaired driving performance and increased RTI risk. It is consistently reported that treatment with continuous positive air pressure (CPAP) improves driving performance and decreases RTI risk, although most of these studies are conducted less than one year after starting treatment. UK law allows treated OSA patients to continue driving if their doctor states that treatment has been successful. Despite the wealth of publications surrounding OSA and driving, 6 key areas were identified from the literature review as not fully investigated, the: (i) prevalence of undiagnosed OSA in heavy goods vehicle (HGV) drivers in the UK; (ii) impact of sleep restriction on long term CPAP treated OSA compared with healthy controls; (iii) ability of treated OSA participants to identify sleepiness when driving; (iv) impact of one night CPAP withdrawal on driving performance; (v) individual difference in driving performance of long term CPAP treated OSA participants; (vi) choice of countermeasures to driver sleepiness by two groups susceptible to driver sleepiness, OSA and HGV drivers. Key areas (i) and (vi) were assessed using questionnaires. 148 HGV drivers were surveyed to assess OSA symptoms and preference of countermeasures to driver sleepiness. All participants completing the driving simulator study were also surveyed. 9.5% of HGV drivers were found to have symptoms of suspected undiagnosed OSA. Additionally the OSA risk factors were more prevalent for HGV drivers than reported in national statistics reports for the general population. The most effective countermeasures to driver sleepiness (caffeine and a nap) were not the most popular. Being part of a susceptible group (OSA or HGV driver) and prior experience of driver sleepiness did not promote effective choice of countermeasure. Key areas (ii) to (v) were assessed using a driving simulator. Driving simulators present a safe environment to test participants in a scenario where they may experience sleepiness without endangering other road users.

Studies have reported that around 20 % of all traffic accidents are caused by a sleepy driver. Sleepy driving has been compared to drunk driving. A sleepy driver is also an issue in the case of automated vehicles in the future. Handing back the control of the vehicle to a sleepy driver is a serious risk. This has increased the need for a sleepiness estimation system that can be used in the car to warn the driver when driving is not recommended. One commonly used method to estimate sleepiness is to study the heart rate variability, HRV, which is said to reflect the activity of the autonomous nervous system, the ANS. The HRV can be expressed through different measures obtained from a signal of RR-intervals. The aim with the thesis is to investigate how well the HRV translates into sleepiness estimation and how the experimental setup might affect the results. In this study, HRV data from 85 sleep deprived drivers was collected together with the drivers’ own ratings of their sleepiness according to the nine graded Karolinska sleepiness scale, KSS. An ANOVA test showed statistical significance for almost all of the used HRV measures when the Driver ID was set as random variable. In order to reduce the number of HRV measures, a feature selection step was performed before training a Support Vector Machine (SVM) used for classification of the data. SVM classifiers are trained to use the input features describing the data to optimize hyperplanes separating the discrete set of classes. Previous research has shown good results in using HRV for sleepiness detection, but common issues are the small data sets used and that most experiments are performed in a simulator instead of at real roads. In some cases, no sleep deprivation is used. The result from the classification in this study is a mean accuracy of around 58-59 %, mean sensitivity of 50-51 %, mean specificity of 75-76 % and mean F1 score of 50-51 % over the three classes ’Alert’, ’Getting sleepy’ and ’Sleepy’. This together with the results of the ANOVA test shows that the HRV measures performed relatively poor when used for classification of the data and that there are large inter-individual differences. This suggests the use of personalized algorithms when developing a sleepiness estimation system and an investigation regarding how other confounding factors could affect the estimation is also motivated.

There is a lot of research conducted on how vehicle drivers become worse when they sleep less. A vehicle driver who sleeps for six hours is 30% more likely to be responsible for vehicle accident, which makes a strict application of the Swedish armed forces rules for drivers rest (6 hours) dangerous. The research conducted on vehicle drivers is made in civilian envionments or in laboratory environments, not military environments. The military environment, wether it is in war zones or during field exercises, will lead to increased stress and poorer sleep for the individual. No research is currently conducted on how military drivers ability to operate a vehicle is affected by the military environment. This thesis aim is to explore this gap in research.  The research question is ‘Does military personnel sleep enough to safely operate a vehicle in a military environment?’. The question is asked in the context of military environments and is answered by conducting experiments on conscripted soldiers who operate a vehicle while conducting a field exercise. The experiment uses 5 participants who drive for 45 minutes along a highway at three separate occasions. The participants are analyzed with an eye tracking system, which assess wether or not the participant is fatigued or distracted.  The result did not give a conclusive answer. At the first occasion all participants participated in the experiment, the results where that they had increased fatigued for 19,8% of the time they spent operating the vehicle. During occasion 2/3 there were three participants of 5, where from only two participants could be used. The results shows that the participant experience drivers fatigue earlier at later occasions, but never so fatigued as to pose a traffic safety risk. This result is wildly inconclusive due to the low participation rate of occasion 2 and 3. When the participants had sun in their eyes the eye tracking system seems prone to assess the participants as more fatigued than they are. The conclusions from this experiment is that more data is needed to draw any conclusions of quality, but it seems as though the participants become more fatigued earlier the longer they are in a military environment. / Det finns mycket forskning som fokuserar på hur mycket sämre trötta förare är när de kör bil. En förare som har sovit i sex timmar har 30% högre sannolikhet att vara ansvarig för en trafikolycka, vilket gör Försvarsmaktens lägsta nivå för förarvila (6 timmar) farlig. Den forskning som genomförs på fordonsförare är gjord på förare i civila miljöer eller i labbmiljöer, inte  i militära miljöer. Den militära miljön, oavsett om det är i krigszoner eller under fältövningar, kommer bidra till ökad stress och sämre sömn för individer. Ingen forskning är i nuläget gjord på hur militära förares framförande av fordon påverkas av den militära miljön. Denna uppsats utforskar denna forskningslucka. Frågeställningen är  ’Sover militära förare tillräckligt mycket för att kunna framföra ett fordon säkert i militär miljö?[KE1] ’. Frågan ställs i föhållande till den miljön som militärer är i och besvaras genom experiment där värnpliktiga förare framför ett fordon när de genomför en fältövning. Experimentet går ut på att fem deltagare kör en personbil i ca 45 minuter längs med en motorväg vid tre tillfällen. Deltagarna analyseras med hjälp av ett eye tracking system, som bedömer hur trötta förarna är och om de är distraherade när de kör. Resultatet gav inte ett entydigt svar. Vid tillfälle 1 deltog samtliga fem deltagare och resultatet gav i genomsnitt att förarna hade förhöjda trötthetsnivåer 19,8% av körtiden. Vid tillfälle 2 och tillfälle 3 kunde endast data från två deltagare användas varav tre deltagare deltog. Datan från tillfälle 2 och 3 pekar på att förarna blev tröttare, men var aldrig farligt trötta under en längre period. På grund av begränsad mängd data kan inga definitiva  slutsatser dras av resultatet. När deltagarna hade sol i ögonen verkade systemet analysera förarna som mer trötta än vad de faktiskt är. Slutsatserna från detta experiment är att mer data behövs för att dra säkra slutsatser, men att det verkar som att förare blir tröttare när de kör bil ju längre in i övningen de kommer.  [KE1]otydligt med en retorisk fråga och titeln på arbetet kopplat mot frågeställningen.