Sleep is a basic need as important as physical fitness and good nutrition. Without enough sleep, we will create a sleep debt and experience sleepiness. Sleepiness can be defined as the inability to stay awake, a condition that has become a health problem in our 24-hour-7-day-a-week society. Estimates suggest that up to one-third of the population suffers from excessive sleepiness. Among other interactions, sleepiness affects our performance, increasing the risk of being involved in accidents. A considerable portion of work related accidents and injuries are related to sleepiness resulting in large costs for the individuals and society. Professional drivers are one example of workers who are at risk of sleepiness related accidents. Up to 40% of heavy truck accidents could be related to sleepiness. A better knowledge about reliable indicators and predictors of sleepiness is important in preventing sleepiness related accidents. This thesis investigates both objective and subjective indicators of sleepiness, how these relate to each other, and how their pattern changes over time. The indicators investigated were electroencephalography, heart rate variability, simple reaction time, head movement, and subjective ratings of sleepiness (Study I-IV). In Study V, a questionnaire study was conducted with professional drivers in northern Sweden. This study mainly deals with predictors of sleepiness. When subjects were sleep deprived both objective and subjective ratings indicated a rapid increase in sleepiness during the first hour of the test followed by a levelling off. This change in pattern was evident for all the indicators except heart rate and heart rate variability. On the other hand, HRV was correlated with the increase of EEG parameters during the post-test sleep period. The changes in pattern of the indicators included in the thesis are analysed in the perspective of temporal patterns and relationships. Of the tested indicators, a subjective rating of sleepiness with CR-10 was considered to be the most reliable indicator of sleepiness. Of the investigated predictors of sleepiness, prior sleep habits were found to be strongly associated to sleepiness and the sleepiness related symptoms while driving. The influences of driving conditions and individual characteristics on sleepiness while driving were lower. A multidisciplinary approach when investigating and implementing indicators and predictors of sleepiness is important. In addition to their actual relations to the development of sleepiness, factors such as technical and practical limitations, work, and individual and situational needs must be taken into account.

The scope of the current research work was to evaluate the potential of the latest version of Microsoft Kinect sensor (Kinect v2) as an external tracking device for head motion during brain imaging with brain Positron Emission Tomography (PET). Head movements constitute a serious degradation factor in the acquired PET images. Although there are algorithms implementing motion correction using known motion data, the lack of effective and reliable motion tracking hardware has prevented their widespread adoption. Thus, the development of effective external tracking instrumentation is a necessity. Kinect was tested both for Siemens High-Resolution Research Tomograph (HRRT) and for Siemens ECAT HR PET system. The face Application Programming Interface (API) ’HD face’ released by Microsoft in June 2015 was modified and used in Matlab environment. Multiple experimental sessions took place examining the head tracking accuracy of kinect both in translational and rotational movements of the head. The results were analyzed statistically using one-sample Ttests with the significance level set to 5%. It was found that kinect v2 can track the head with a mean spatial accuracy of µ0 < 1 mm (SD = 0,8 mm) in the y-direction of the tomograph’s camera, µ0 < 3 mm (SD = 1,5 mm) in the z-direction of the tomograph’s camera and µ0 < 1 ◦ (SD < 1 ◦ ) for all the angles. However, further validation needs to take place. Modifications are needed in order for kinect to be used when acquiring PET data with the HRRT system. The small size of HRRT’s gantry (over 30 cm in diameter) makes kinect’s tracking unstable when the whole head is inside the gantry. On the other hand, Kinect could be used to track the motion of the head inside the gantry of the HR system.