Integrating eye tracking in virtual reality for stroke rehabilitation

This thesis reports on research done for the integration of eye tracking technology into virtual reality environments, with the goal of using it in rehabilitation of patients who suffered from stroke. For the last few years, eye tracking has been a focus on medical research, used as an assistive tool  to help people with disabilities interact with new technologies  and as an assessment tool  to track the eye gaze during computer interactions. However, tracking more complex gaze behaviors and relating them to motor deficits in people with disabilities is an area that has not been fully explored, therefore it became the focal point of this research. During the research, two exploratory studies were performed in which eye tracking technology was integrated in the context of a newly created virtual reality task to assess the impact of stroke. Using an eye tracking device and a custom virtual task, the system developed is able to monitor the eye gaze pattern changes over time in patients with stroke, as well as allowing their eye gaze to function as an input for the task. Based on neuroscientific hypotheses of upper limb motor control, the studies aimed at verifying the differences in gaze patterns during the observation and execution of the virtual goal-oriented task in stroke patients (N=10), and also to assess normal gaze behavior in healthy participants (N=20). Results were found consistent and supported the hypotheses formulated, showing that eye gaze could be used as a valid assessment tool on these patients. However, the findings of this first exploratory approach are limited in order to fully understand the effect of stroke on eye gaze behavior. Therefore, a novel model-driven paradigm is proposed to further understand the relation between the neuronal mechanisms underlying goal-oriented actions and eye gaze behavior.

Prediction of adult height in girls: the Beunen-MalinaFreitas method

The purpose of this study was to validate and cross-validate the Beunen-Malina-Freitas method for non-invasive prediction of adult height in girls. A sample of 420 girls aged 10–15 years from the Madeira Growth Study were measured at yearly intervals and then 8 years later. Anthropometric dimensions (lengths, breadths, circumferences, and skinfolds) were measured; skeletal age was assessed using the Tanner-Whitehouse 3 method and menarcheal status (present or absent) was recorded. Adult height was measured and predicted using stepwise, forward, and maximum R2 regression techniques. Multiple correlations, mean differences, standard errors of prediction, and error boundaries were calculated. A sample of the Leuven Longitudinal Twin Study was used to cross-validate the regressions. Age-specific coefficients of determination (R2) between predicted and measured adult height varied between 0.57 and 0.96, while standard errors of prediction varied between 1.1 and 3.9 cm. The cross-validation confirmed the validity of the Beunen-Malina-Freitas method in girls aged 12–15 years, but at lower ages the cross-validation was less consistent. We conclude that the Beunen-Malina-Freitas method is valid for the prediction of adult height in girls aged 12–15 years. It is applicable to European populations or populations of European ancestry.