A novel method for EOG features extraction from the forehead

Abstract — We have shown that the slow eye movements extracted from electrooculogram (EOG) signals can be used to estimate human vigilance in our previous work. However, the traditional method for recording EOG signals is to place the electrodes near the eyes of subjects. This placement is inconvenient for users in real-world applications. This paper aims to find a more practical placement for acquiring EOG signals for vigilance estimation. Instead of placing the electrodes near the eyes, we place them on the forehead. We extract EOG features from the forehead EOG signals using both independent component analysis and support vector machines. The performance of our proposed method is evaluated using the correlation coefficients between the forehead EOG signals and the traditional EOG signals. The results show that a correlation of 0.84 can be obtained when the users make 14 different face movements and for merely eye movements it reaches 0.93. I

helmet-based physiological signal monitoring system

This paper describes helmet-based wearable biosignal monitoring system that can measure ECG, EOG, EEG alpha wave and shows its application for detection of drowsiness.authors' final draftA helmet-based system that was able to monitor the drowsiness of a soldier was developed. The helmet system monitored the electrocardiogram, electrooculogram and electroencephalogram (alpha waves) without constraints. Six dry electrodes were mounted at five locations on the helmet: both temporal sides, forehead region and upper and lower jaw strips. The electrodes were connected to an amplifier that transferred signals to a laptop computer via Bluetooth wireless communication. The system was validated by comparing the signal quality with conventional recording methods. Data were acquired from three healthy male volunteers for 12 min twice a day whilst they were sitting in a chair wearing the sensor-installed helmet. Experimental results showed that physiological signals for the helmet user were measured with acceptable quality without any intrusions on physical activities. The helmet system discriminated between the alert and drowsiness states by detecting blinking and heart rate variability (HRV) parameters extracted from ECG. Blinking duration and eye reopening time were increased during the sleepiness state compared to the alert state. Also, positive peak values of the sleepiness state were much higher, and the negative peaks were much lower than that of the alert state. The LF/HF ratio also decreased during drowsiness. This study shows the feasibility for using this helmet system: the subjects health status and mental states could be monitored without constraints whilst they were working.This study was supported by a grant from the Advanced Biometric Research Center (ABRC) and the Korea Science and Engineering Foundation (KOSEF)

Robotereinsatz in der werkstattorientierten Fertigung

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Adaptive wake and sleep detection for wearable systems

Sleep problems and disorders have a serious impact on human health and wellbeing. The rising costs for treating sleep-related chronic diseases in industrialized countries demands efficient prevention. Low-cost, wearable sleep / wake detection systems which give feedback on the wearer's "sleep performance" are a promising approach to reduce the risk of developing serious sleep disorders and fatigue. Not all bio-medical signals that are useful for sleep / wake discrimination can be easily recorded with wearable systems. Sensors often need to be placed in an obtrusive location on the body or cannot be efficiently embedded into a wearable frame. Furthermore, wearable systems have limited computational and energetic resources, which restrict the choice of sensors and algorithms for online processing and classification. Since wearable systems are used outside the laboratory, the recorded signals tend to be corrupted with additional noise that influences the precision of classification algorithms. In this thesis we present the research on a wearable sleep / wake classifier system that relies on cardiorespiratory (ECG and respiratory effort) and activity recordings and that works autonomously with minimal user interaction. This research included the selection of optimal signals and sensors, the development of a custom-tailored hardware demonstrator with embedded classification algorithms, and the realization of experiments in real-world environments for the customization and validation of the system. The processing and classification of the signals were based on Fourier transformations and artificial neural networks that are efficiently implementable into digital signal controllers. Literature analysis and empiric measurements revealed that cardiorespiratory signals are more promising for a wearable sleep / wake classification than clinically used signals such as brain potentials. The experiments conducted during this thesis showed that inter-subject differences within the recorded physiological signals make it difficult to design a sleep / wake classification model that can generalize to a group of subjects. This problem was addressed in two ways: First by adding features from another signal to the classifier, that is, measuring the behavioral quiescence during sleep using accelerometers. Conducted research on different feature extraction methods from accelerometer data showed that this data generalizes well for distinct subjects in the study group. In addition, research on user-adaptation methods was conducted. Behavioral sleep and wake measures, notably the measurement of reactivity and activity, were developed to build up a priori knowledge that was used to adapt the classification algorithm automatically to new situations. This thesis demonstrates the design and development of a low-cost, wearable hardware and embedded software for on-line sleep / wake discrimination. The proposed automatic user-adaptive classifier is advantageous compared to previously suggested classification methods that generalize over multiple subjects, because it can take changes in the wearer's physiology and sleep / wake behavior into account without adjustment from a human expert. The results of this thesis contribute to the development of smart, wearable, bio-physiological monitoring systems which require a high degree of autonomy and have only low computational resources available. We believe that the proposed sleep / wake classification system is a first promising step toward a context-aware system for sleep management, sleep disorder prevention, and reduction of fatigue