In electronic health (eHealth) research, limited insight has been obtained on process outcomes or how the use of technology has contributed to the users’ ability to have a healthier life, improved well-being, or activate new attitudes in their daily tasks. As a result, eHealth is often perceived as a black box. To open this black box of eHealth, methodologies must extend beyond the classic effect evaluations. The analyses of log data (anonymous records of real-time actions performed by each user) can provide continuous and objective insights into the actual usage of the technology. However, the possibilities of log data in eHealth research have not been exploited to their fullest extent. The aim of this paper is to describe how log data can be used to improve the evaluation and understand the use of eHealth technology with a broader approach than only descriptive statistics. This paper serves as a starting point for using log data analysis in eHealth research. Here, we describe what log data is and provide an overview of research questions to evaluate the system, the context, the users of a technology, as well as the underpinning theoretical constructs. We also explain the requirements for log data, the starting points for the data preparation, and methods for data collection. Finally, we describe methods for data analysis and draw a conclusion regarding the importance of the results for both scientific and practical applications. The analysis of log data can be of great value for opening the black box of eHealth. A deliberate log data analysis can give new insights into how the usage of the technology contributes to found effects and can thereby help to improve the persuasiveness and effectiveness of eHealth technology and the underpinning behavioral models.

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Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and become one of the enabling technologies for disaster early-warning systems. Event detection functionality of WSNs can be of great help and importance for (near) real-time detection of, for example, meteorological natural hazards and wild and residential fires. From the data-mining perspective, many real world events exhibit specific patterns, which can be detected by applying machine learning (ML) techniques. In this paper, we introduce ML techniques for distributed event detection in WSNs and evaluate their performance and applicability for early detection of disasters, specifically residential fires. To this end, we present a distributed event detection approach incorporating a novel reputation-based voting and the decision tree and evaluate its performance in terms of detection accuracy and time complexity. Keywords -Disaster early warning systems, event detection, wireless sensor networksI.

Touch behavior is of great importance during social interaction. To transfer the tactile modality from interpersonal interaction to other areas such as Human-Robot Interaction (HRI) and remote communication automatic recognition of social touch is necessary. This paper introduces CoST: Corpus of Social Touch, a collection containing 7805 instances of 14 different social touch gestures. The gestures were performed in three variations: gentle, normal and rough, on a sensor grid wrapped around a mannequin arm. Recognition of the rough variations of these 14 gesture classes using Bayesian classifiers and Support Vector Machines (SVMs) resulted in an overall accuracy of 54% and 53%, respectively. Furthermore, this paper provides more insight into the challenges of automatic recognition of social touch gestures, including which gestures can be recognized more easily and which are more difficult to recognize.

Brain-computer interfaces (BCIs) are not only being developed to aid disabled individuals with motor substitution, motor recovery, and novel communication possibilities, but also as a modality for healthy users in entertainment and gaming. This study investigates whether the incorporation of a BCI in the popular game World of Warcraft (WoW) has effects on the user experience. A BCI control channel based on parietal alpha band power is used to control the shape and function of the avatar in the game. In the experiment, participants , a mix of experienced and inexperienced WoW players, played with and without the use of BCI in a within-subjects design. Participants themselves could indicate when they wanted to stop playing. Actual and estimated duration was recorded and questionnaires on presence and control were administered. Afterwards, oral interviews were taken. No difference in actual duration was found between conditions. Results indicate that the difference between estimated and actual duration was not related to user experience but was person specific. When using a BCI, control and involvement were rated lower. But BCI control did not significantly decrease fun. During interviews, experienced players stated that they saw potential in the application of BCIs in games with complex interfaces such as WoW. This study suggests that BCI as an additional control can be as much fun and natural to use as keyboard/mouse control, even if the amount of control is limited.