• Journal of Digital Convergence
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• v.8 no.2
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• pp.203-216
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• 2010

This study corresponds to an exploratory survey that identifies and organizes important decision factors for establishing R&D strategic portfolio in the application of digitalized Korean human-dataset. In the case of countries that have performed the above, the digitalized human-dataset and its visualization application development research are regarded as strategic R&D projects selected and supervised in national level. To achieve the goal of this study, we organize a professional group that reviews articles, suggests research topics, considers alternatives and answers questionnaires. With this study, we draw and refine the detailed factors; these are reflected during a strategic planning phase that includes R&D vision setting, SWOT analysis and strategy development, research area and project selection. In addition to this contribution for supporting the strategic planning, the study also shows the detailed research area's definition/scope and their priorities in terms of importance and urgency. This addition will act as a guideline for investigating further research and as a framework for assessing the current status of research investment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.127-133
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• 2021

A recent study of people physically falling focused on analyzing the motions of the falls using a recurrent neural network (RNN) and a deep learning approach to get good results from detecting 2D human poses from a single color image. In this paper, we investigate a detection method for estimating the position of the head and shoulder keypoints and the acceleration of positional change using the skeletal keypoints information extracted using PoseNet from an image obtained with a low-cost 2D RGB camera, increasing the accuracy of judgments about the falls. In particular, we propose a fall detection method based on the characteristics of post-fall posture in the fall motion-analysis method. A public data set was used to extract human skeletal features, and as a result of an experiment to find a feature extraction method that can achieve high classification accuracy, the proposed method showed a 99.8% success rate in detecting falls more effectively than a conventional, primitive skeletal data-use method.