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This work presents a novel and versatile approach to employ textile capacitive sensing as an effective solution for capturing human body movement through fashionable and everyday-life garments. Conductive textile patches are utilized for sensing the movement, working without the need for strain or direct body contact, wherefore the patches can sense only from their deformation within the garment. This principle allows the sensing area to be decoupled from the wearer's body for improved wearing comfort and more pleasant integration. We demonstrate our technology based on multiple prototypes which have been developed by an interdisciplinary team of electrical engineers, computer scientists, digital artists, and smart fashion designers through several iterations to seamlessly incorporate the technology of capacitive sensing with corresponding design considerations into textile materials. The resulting accumulation of textile capacitive sensing wearables showcases the versatile application possibilities of our technology from single-joint angle measurements towards multi-joint body part tracking.

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Coronary Artery Disease is the leading cause of death worldwide. Its prevalence will grow while access to specialized medical care will be further limited due to staff shortages. Therefore, any facilitation of diagnosis or treatment is of paramount importance. The diagnosis based on Coronary Angiography can be automated to perform a quantitative evaluation of lesions. This requires precise segmentation of coronary arteries. At the moment, the state-of-the-art algorithms fail to eliminate vessel-like artifacts that are wrongly included in segmentation results (e.g. catheters, stitches). This is a bottleneck for the automatization of the diagnosis workflow that precedes clinical action. In this paper, we propose a 2-step post-segmentation refinement algorithm. A binary segmentation of the coronary arteries is used to extract image features - inputs for an XGBoost Classifier. Its predictions are improved by a neighborhood filter that leverages contextual information to assign correct labels. The algorithm is primarily concerned with differentiating vessels from other vessel-like objects and does so with a 99% accuracy rate. It takes advantage of an original local description of Tamura features, which proved to be one of the most influential factors in decision-making. As a result, the segmentation of coronary arteries is cleaned from artifacts, enabling AI-supported diagnosis workflows to be automated. After re-training, the proposed method can be used to eliminate post-segmentation artifacts in other medical domains.Clinical relevance- The algorithm proposed in this paper allows for the development of software that could automatically calculate the Syntax Score in real time. This would shorten diagnostics time and allow for immediate action in critical cases.

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PURPOSE: Early detection of tumors and their spread, particularly in lymph node illnesses, is critical for a full recovery. However, it is currently difficult due to a lack of imaging or detection devices that provide the necessary spatial depth and location information. Consequently, it would be beneficial to have a simple and cost-effective sensor device to determine the 3D position of, e.g., a lymph node in the patient's coordinate system. METHODS: In this work, we present a concept and design for a novel semiconductor-based 3D detection system that uses inexpensive off-the-shelf components to measure gamma activity. A simple Arduino-type microcontroller calculates the 3D position of the probe based on the number of the measured pulse, the spatial sensitivity characteristics, and the known geometry of the device. RESULTS: The system was set up from four photodiodes (Osram BPW34), a transistor-based pre-amplifier, and a two-stage operational amplifier as the main stage. Doing so, a signal sufficient to be read by the microcontroller could be produced. The performed calculations proved that for a system consisting of at least four photodiodes, it is possible to determine precise location of a gamma radiation source. CONCLUSIONS: After successful first experiments with a single diode, the optimal spatial arrangement of the diodes as well as their orientation will be determined to achieve a compact, cost effective yet fast, and accurate sensor device for every-day clinical application.