Anxiety among pregnant women can significantly impact their overall well-being. However, the development of data-driven HCI interventions for this demographic is often hindered by data scarcity and collection challenges. In this study, we leverage the Empatica E4 wristband to gather physiological data from pregnant women in both resting and relaxed states. Additionally, we collect subjective reports on their anxiety levels. We integrate features from signals including Blood Volume Pulse (BVP), Skin Temperature (SKT), and Inter-Beat Interval (IBI). Employing a Support Vector Machine (SVM) algorithm, we construct a model capable of evaluating anxiety levels in pregnant women. Our model attains an emotion recognition accuracy of 69.3%, marking achievements in HCI technology tailored for this specific user group. Furthermore, we introduce conceptual ideas for biofeedback on maternal emotions and its interactive mechanism, shedding light on improved monitoring and timely intervention strategies to enhance the emotional health of pregnant women.
BackgroundScientific software incorporates models that capture fundamental domain knowledge. This software is becoming increasingly more relevant as an instrument for food research. However, scientific software is currently hardly shared among and (re-)used by stakeholders in the food domain, which hampers effective dissemination of knowledge, i.e. knowledge transfer.Scope and approachThis paper reviews selected approaches, best practices, hurdles and limitations regarding knowledge transfer via software and the mathematical models embedded in it to provide points of reference for the food community.Key findings and conclusionsThe paper focusses on three aspects. Firstly, the publication of digital objects on the web, which offers valorisation software as a scientific asset. Secondly, building transferrable software as way to share knowledge through collaboration with experts and stakeholders. Thirdly, developing food engineers' modelling skills through the use of food models and software in education and training.
The Design-to-Robotic-Production and -Assembly (D2RP&A) process developed at Delft University of Technology (DUT) has been scaled up to building size by prototyping of-site a 3.30 m high fragment of a larger spaceframe structure The fragment consists of wooden linear elements connected to a polymer node printed at 3D Robot Printing and panels robotically milled at Amsterdam University of Applied Science (AUAS). It has been evaluated for suitability for assembly on-site without temporary support while relying on human-robot collaboration. The constructed architectural hybrid structure is proof of concept for an on- and off-site D2RP&A approach that is envisioned to be implemented using a range of robots able to possibly address all phases of construction in the future.
