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In the intensive care unit (ICU), patients are often sedated and ventilated, which makes clinical evaluation of the status of the brain very limited. In these situations, the EEG is a potentially very useful tool to be informed about the status of the brain. EEG rhythms are very sensitive for derangements in brain function, as may occur in hypoxia, and can reliably detect epileptiform discharges. In addition, it can be a valuable tool for follow-up of patients, and can significantly contribute to prognostication of brain-damaged patients. The actual use of continuous EEG monitoring in the ICU is limited, however. An important contributing factor is related to the difficulties in interpreting the raw EEG signal, that does not permit a straightforward understanding by nontrained personnel. We recently proposed a visual transform of the EEG (van Putten, J Clin Neurophysiology 2008; 25:63–68) to facilitate the interpretation. Here, we present a modification of this analysis, including examples of this method in monitoring patients in the ICU.

Tremor and bradykinesia are two prominent features of Parkinson's disease (PD). This paper presents the PD monitor, a measurement system using accelerometry to quantify tremor and bradykinesia. A correlation analysis of the physician's scores and the PD monitor demonstrated that the system accurately measured the severity of tremor and bradykinesia whilts assessing a broad spectrum of aspects related to both symptoms. In conclusion the PD monitor can be used for a detailed evaluation of the PD monitor symptoms in order to optimize treatment.

Deep Brain Stimulation (DBS) in subthalamic nucleus (STN) has substantially improved the suppression of symptoms in Parkinson's disease (PD). However, a quantitative understanding of the high-frequency stimulation opn the neuronal elements surrounding the electrode and the resulting effects for the different symptoms for upper and lower extremities is missing. The aim of the study is to investigate if there is a somatotopic organisation of the STN.

An important part in the analysis of human activity video data is the silhouette segmentation. The results of segmentation are greatly affected by imaging environment, thus posing a problem for the extraction of the region in the image containing a subject of interest. In this paper we propose a human motion analysis method based on nonparametric clustering of monocular color images. Motivated by the need to automatically extract human silhouettes for kinematic gait analysis, without spurious segmentations naturally occurring in other segmentation methods, we have developed and applied adaptive mesh-based color clustering which can be combined with motion segmentation. The advantage of our method is that it is controlled by few intuitive parameters allowing the method to be adjusted to different capturing environments. The usage of color is not sensitive to illumination variations, or to different color distributions of different cameras, as the color distribution is compared between different regions of the same frame. To provide a qualitative evaluation of our method, our results are compared with the Gaussian Mixture model, a standard vision-based human subject extraction method based on background segmentation techniques.

Rehabilitation robots can support the training of patients with neurological gait disorders. Classical control approaches permit patients to remain passive during the robot-assisted training. We hypothesize that promoting active participation of patients will improve training efficacy. In this paper, we evaluate the combination of two existing patient-cooperative control strategies. These strategies are applicable to robotic exoskeletons that assist a patient walking on a treadmill. The first strategy, Automatic treadmill speed adaptation, detects the patient’s desired walking speed and accelerates the treadmill accordingly. The second strategy, Path control, provides spatial guidance of the legs via the rehabilitation robot while the patient controls the timing of his/her movements. We demonstrate that both strategies can be successfully combined towards an approach that allows subjects to walk on their own with the support of a robot instead of being passively moved.