In this paper we present work in progress on SiFEB, a simple, interactive and extensible robot playmate for kids intended to nurture the engagement in STEM (Science, Technology, Engineering and Mathematics) related activities through playing. SiFEB's simplified and interactive hardware and software modules present users without prior experience, with the ability to build robots without going deep in either aspect. Kids can build robots of their interest using attachable hardware modules and related programming components using an easy to understand visual programming language that resembles natural commands. The potential developers may utilize the framework provided, to build hardware and software components to the system expanding the existing capabilities. Preliminary results from the prototypes show that SiFEB has the potential of being an effective learning platform for young children in STEM related areas.
Recently, interest has grown in the assessment of anxiety that leverages human physiological and behavioral data to address the drawbacks of current subjective clinical assessments. Complex experiences of anxiety vary on multiple characteristics, including triggers, responses, duration and severity, and impact differently on the risk of anxiety disorders. This article reviews the past decade of studies that objectively analyzed various anxiety characteristics related to five common anxiety disorders in adults utilizing features of cardiac, electrodermal, blood pressure, respiratory, vocal, posture, movement and eye metrics. Its originality lies in the synthesis and interpretation of consistently discovered heterogeneous predictors of anxiety and multimodal-multisensor analytics based on them. We reveal that few anxiety characteristics have been evaluated using multimodal-multisensor metrics, and many of the identified predictive features are confounded. As such, objective anxiety assessments are not yet complete or precise. That said, few multimodal-multisensor systems evaluated indicate an approximately 11.73% performance gain compared to unimodal systems, highlighting a promising powerful tool. We suggest six high-priority future directions to address the current gaps and limitations in infrastructure, basic knowledge and application areas. Action in these directions will expedite the discovery of rich, accurate, continuous and objective assessments and their use in impactful end-user applications.
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