Eye-tracking is a key technology for future retinal projection based AR glasses as it enables techniques such as foveated rendering or gaze-driven exit pupil steering, which both increases the system’s overall performance. However, two of the major challenges video oculography systems face are robust gaze estimation in the presence of glasses slippage, paired with the necessity of frequent sensor calibration. To overcome these challenges, we propose a novel, calibration-free eye-tracking sensor for AR glasses based on a highly transparent holographic optical element (HOE) and a laser scanner. We fabricate a segmented HOE generating two stereo images of the eye-region. A single-pixel detector in combination with our stereo reconstruction algorithm is used to precisely calculate the gaze position. In our laboratory setup we demonstrate a calibration-free accuracy of 1.35° achieved by our eye-tracking sensor; highlighting the sensor’s suitability for consumer AR glasses.

After the success of smartphones and smartwatches, smart glasses are expected to be the next smart wearable. While novel display technology allows the seamlessly embedding of content into the FOV, interaction methods with glasses, requiring the user for active interaction, limiting the user experience. One way to improve this and drive immersive augmentation is to reduce user interactions to a necessary minimum by adding context awareness to smart glasses. For this, we propose an approach based on human activity recognition, which incorporates features, derived from the user's head- and eye-movement. Towards this goal, we combine an commercial eye-tracker and an IMU to capture eye- and head-movement features of 7 activities performed by 20 participants. From a methodological perspective, we introduce U-HAR, a convolutional network optimized for activity recognition. By applying a few-shot learning, our model reaches an macro-F1-score of 86.59%, allowing us to derive contextual information.

Robust and highly integrated eye-tracking is a key technology to improve resolution of near-eye-display technologies for augmented reality (AR) glasses such as focus-free retinal projection as it enables display enhancements like foveated rendering. Furthermore, eye-tracking sensors enables novel ways to interact with user interfaces of AR glasses, improving thus the user experience compared to other wearables. In this work, we present a novel approach to track the user's eye by scanned laser feedback interferometry sensing. The main advantages over modern video-oculography (VOG) systems are the seamless integration of the eye-tracking sensor and the excellent robustness to ambient light with significantly lower power consumption. We further present an algorithm to track the bright pupil signal captured by our sensor with a significantly lower computational effort compared to VOG systems. We evaluate a prototype to prove the high robustness against ambient light and achieve a gaze accuracy of 1.62\,$^\circ$, which is comparable to other state-of-the-art scanned laser eye-tracking sensors. The outstanding robustness and high integrability of the proposed sensor will pave the way for everyday eye-tracking in consumer AR glasses.

During the last years the significance of RFID systems has increased rapidly. In fact, its use is expected to increase by a factor of twenty until 2016. Medical applications are expected to be a main cause of this development. Currently, RFID systems are primary identification systems, based on the transmission of a key number or little further information. Using new technical developements and higher integration levels, it may be possible to extend these systems to measure and transmit data. Such RFID systems can be used in different areas of medical technology, because of the combination of application flexibility with the possibility of identification and data collection. For example, they can be used to identify patients or pharmaceuticals, monitor blood preservations or for medical implant communication.

Early detection of lung edema for patients suffering from chronic heart disease improves the medical treatment and can avoid committal of the patient to an intensive care unit. Therefore, an early warning system monitoring the amount of fluid in the lungs by measuring trans-thoracic bioimpedance outside the body has been developed. The proposed system(TiBIS) consists of a textile integrated measurement module and a Personal Digital Assistant for signal processing and user interaction.