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The Wearables for Epilepsy And Research (WEAR) International Study Group identified a set of methodology standards to guide research on wearable devices for seizure detection. We formed an international consortium of experts from clinical research, engineering, computer science, and data analytics at the beginning of 2020. The study protocols and practical experience acquired during the development of wearable research studies were discussed and analyzed during bi-weekly virtual meetings to highlight commonalities, strengths, and weaknesses, and to formulate recommendations. Seven major essential components of the experimental design were identified, and recommendations were formulated about: (1) description of study aims, (2) policies and agreements, (3) study population, (4) data collection and technical infrastructure, (5) devices, (6) reporting results, and (7) data sharing. Introducing a framework of methodology standards promotes optimal, accurate, and consistent data collection. It also guarantees that studies are generalizable and comparable, and that results can be replicated, validated, and shared.

Given the rapid growth of social media websites and the ease of aggregating ever-richer social data, an inevitable research question that can be expected to emerge is whether different interaction patterns of individuals and their meaningful interpretation can be captured for social network analysis. In this work, we present a novel solution that discovers occurrences of prototypical 'ego network' patterns from social media and mobile-phone networks, to provide a data-driven instrument to be used in behavioral sciences for graph interpretations. We analyze nine datasets gathered from social media websites and mobile phones, together with 13 network measures, and three unsupervised clustering algorithms. Further, we use an unsupervised feature similarity technique to reduce redundancy and extract compact features from the data. The reduced feature subsets are then used to discover ego patterns using various clustering techniques. By cluster analysis, we discover that eight distinct ego neighborhood patterns or ego graphs have emerged. This categorization allows concise analysis of users' data as they change over time. We provide fine-grained analysis for the validity and quality of clustering results. We perform clustering verification based on the following three intuitions: i) analyzing the clustering patterns for the same set of users crawled from three social media networks, ii) associating metadata information with the clusters and evaluating their performance on real networks, iii) studying selected participants over an extended period to analyze their behavior.

Health assistant tools at the workplace may contribute to preventing work-related absenteeism, increasing overall employee satisfaction, and reducing the costs of sickness or presenteeism in the long term. The tools may be integrated into a digital corporate health management strategy. Despite their huge potential, a major drawback of common tools (e.g., wearables, dedicated cameras) are that they require direct interaction, skin contact, or come with a high acquisition cost. A concept for unobtrusive and software-based monitoring to increase long-term health, improve working conditions or show the necessary adjustments to the new work situation can help to solve these problems. This paper presents a concept that shows how a simple webcam can be utilized to record vital signs, posture, and behavior during working hours. It offers individual and intelligent interven-tions and recommendations based on these data to reduce psychological and physical stress. Our approach demonstrates that the required parameters can be used to offer user-tailored interventions based on simple rules. We present a pro-totypical implementation of an intelligent health companion and show avenues for future research.