[Show abstract][Hide abstract] ABSTRACT: The segmentation of gait signals into single steps is an important basis for objective gait analysis. Only a precise detection of step beginning and end enables the computation of step parameters like step height, variability and duration. A special challenge for the application is the accurateness of such an algorithm when based on signals from daily live activities. In this study, gyroscopes were attached laterally to sport shoes to collect gait data. For the automated step segmentation, subsequence Dynamic Time Warping was used. 35 healthy controls and ten patients with Parkinson's disease performed a four times ten meter walk. Furthermore 4 subjects were recorded during different daily life activities. The algorithm enabled counting steps, detecting precisely step beginning and end and rejecting other movements. Results showed a recognition rate of steps during ten meter walk exercises of 97.7% and in daily life activities of 86.7%. The segmentation procedure can be used for gait analysis from daily life activities and can constitute the basis for computation of precise step parameters. The algorithm is applicable for long-term gait monitoring as well as for analyzing gait abnormalities.
Full-text · Article · Jul 2013 · Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
[Show abstract][Hide abstract] ABSTRACT: Objective and rater independent analysis of movement impairment is one of the most challenging tasks in medical engineering. Especially assessment of motor symptoms defines the clinical diagnosis in Parkinson's disease (PD). A sensor-based system to measure the movement of the upper and lower extremities would therefore complement the clinical evaluation of PD. In this study two different sensor-based systems were combined to assess movement of 18 PD patients and 17 healthy controls. First, hand motor function was evaluated using a sensor pen with integrated accelerometers and pressure sensors, and second, gait function was assessed using a sports shoe with attached inertial sensors (gyroscopes,accelerometers).Subjects performed standardized tests for both extremities.Features were calculated from sensor signals to differentiate between patients and controls. For the latter, pattern recognition methods were used and the performance of four classifiers was compared. In a first step classification was done for every single system and in a second step for combined features of both systems. Combination of both motor task assessments substantially improved classification rates to 97%using the AdaBoost classifier for the experiment patients vs.controls.The combination of two different analysis systems led to enhanced, more stable, objective, and rater independent recognition of motor impairment. The method can be used as a complementary diagnostic tool for movement disorders.
No preview · Article · Aug 2012 · Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
[Show abstract][Hide abstract] ABSTRACT: Olfactory impairment is a consistent premotor symptom in sporadic Parkinson's disease (PD), presumably caused by pathological processes in the olfactory bulb and olfactory structures within mesolimbic brain areas. The objective of the present study was to obtain an in-depth insight into olfactory network dysfunction in PD patients. Event-related functional magnetic resonance imaging (3 T) was conducted with 16 early-stage PD patients and 16 matched controls during an odor detection task. Activation within the olfactory network was analyzed both in terms of strength of activation (whole-brain random effects, regions of interest [ROI] analysis based on the hemodynamic response function) as well as time-course characteristics (finite impulse response-based ROI analysis). Olfactory-induced activation in patients with PD in comparison to a standard activation pattern obtained from controls revealed profound hyperactivation in piriform and orbitofrontal cortices. However, whereas orbitofrontal areas seem to be unable to discriminate between signal and noise, primary olfactory cortex shows preserved discriminatory ability. These results support a complex network dysfunction that exceeds structural pathology observed in the olfactory bulb and mesolimbic cortices and thus demonstrate the important contribution of functional data to describe network dynamics occurring in the degenerating brain.