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Clinical scales represent the golden standard in characterizing awareness for patients in vegetative or in a minimally conscious state. Clinical scales suffer from problems of sensitivity, specificity, subjectivity, and inter-rater reliability. This leads to a misdiagnosis rate of up to 40% and consequences associated with inappropriate treatment decisions. In this study, objective measures including physiological and neurological signals are used to quantify the patient status. Using linear backward regression analysis, 13 variables (based on frequency analysis of the electrocardiogram, heart rate variability, amplitude and latency of the P300, skin conductance responses, changes in the blood pressure and respiration signal) were found to be sufficient to describe 74.7% of the variability of the scores. In this regression model, the P300, electrocardiogram and the blood pressure signal account for most of the variability. More patient data and additional measures will enable refinement of the methods. This new objective-measurement based model of the state of awareness will complement the clinical scales in order to increase the quality of diagnosis.

We implemented a model for prediction of heart rate during Lokomat walking. Using this model, we can predict potential overstressing of the patient and adapt the physical load accordingly. Current models for treadmill based heart rate control neglect the fact that the interaction torques between Lokomat and human can have a significant effect on heart rate. Tests with five healthy subjects lead to a model of sixth order with walking speed and power expenditure as inputs and heart rate prediction as output. Recordings with five different subjects were used for model validation. Future work includes model identification and predictive heart rate control with spinal cord injured and stroke patients.