Evaluating sleepiness using force platform posturography.

We have investigated the feasibility to use posturography as a method to estimate sleep deprivation. This manuscript presents a proof-of-concept of this idea. Twenty-one healthy subjects aged 20-37 years participated in the study. The subjects were deprived of sleep for up to 36 h. Their postural stability was measured as a function of sleep deprivation time. As a reference the critical fusion frequency method for measuring sleepiness was used. The 163 posturographic parameters used for analyzing the posturographic data were found from the literature. Of these parameters, the fractal dimension of the sway path, the most common frequency of the sway, the time-interval for open-loop control of stance, and the most common amplitude of the sway showed the highest linear correlations with sleep deprivation time. Using these four parameters we were able to estimate the sleep deprivation time with an accuracy better than 5 h for 80% of the subjects.

Virtual reality in posturography.

Balance dysfunctions are common, especially among elderly people. Present methods for the diagnosis and evaluation of severity of dysfuntion have limited value. We present a system that makes it easy to implement different visual and mechanical perturbations for clinical investigations of balance and visual-vestibular interaction. The system combines virtual reality visual stimulation with force platform posturography on a moving platform. We evaluate our contruction's utility in a classification task between 33 healthy controls and 77 patients with Ménière's disease, using a series of tests with different visual and mechanical stimuli. Responses of patients and controls differ significantly in parameters computed from stabilograms. We also show that the series of tests achieves a classification accuracy slightly over 80% between controls and patients.

Efficient driver drowsiness detection at moderate levels of drowsiness.

Previous research on driver drowsiness detection has focused primarily on lane deviation metrics and high levels of fatigue. The present research sought to develop a method for detecting driver drowsiness at more moderate levels of fatigue, well before accident risk is imminent. Eighty-seven different driver drowsiness detection metrics proposed in the literature were evaluated in two simulated shift work studies with high-fidelity simulator driving in a controlled laboratory environment. Twenty-nine participants were subjected to a night shift condition, which resulted in moderate levels of fatigue; 12 participants were in a day shift condition, which served as control. Ten simulated work days in the study design each included four 30-min driving sessions, during which participants drove a standardized scenario of rural highways. Ten straight and uneventful road segments in each driving session were designated to extract the 87 different driving metrics being evaluated. The dimensionality of the overall data set across all participants, all driving sessions and all road segments was reduced with principal component analysis, which revealed that there were two dominant dimensions: measures of steering wheel variability and measures of lateral lane position variability. The latter correlated most with an independent measure of fatigue, namely performance on a psychomotor vigilance test administered prior to each drive. We replicated our findings across eight curved road segments used for validation in each driving session. Furthermore, we showed that lateral lane position variability could be derived from measured changes in steering wheel angle through a transfer function, reflecting how steering wheel movements change vehicle heading in accordance with the forces acting on the vehicle and the road. This is important given that traditional video-based lane tracking technology is prone to data loss when lane markers are missing, when weather conditions are bad, or in darkness. Our research findings indicated that steering wheel variability provides a basis for developing a cost-effective and easy-to-install alternative technology for in-vehicle driver drowsiness detection at moderate levels of fatigue.

Wavelet analysis to detect gait events.

Manually detecting gait events by visual inspection of gait data is laborious. Currently, there are no robust techniques available to automate the process. However, detecting gait events is essentially a classification problem; an application for which wavelet analysis, a multiresolution technique, is well suited for. We employ wavelet analysis to classify heel strike- and toe off events using the ground reaction forces that are exerted during walking. We recorded the ground reaction forces for 30 unshod healthy subjects while they were stepping in place on a force platform for 30 s at a self-selected pace. Depending on the pace, each subject completed 14-26 gait cycles. We compared the timing of events detected with the wavelet analysis with the timing of events detected by analyzing the signal time-derivative. On average, the wavelet analysis detected the events 29 ms later. This difference corresponds to 1.2% of the average duration of the gait cycles, which was 2.4 s. Wavelet analysis shows promise for automated detection of gait events.