We present a wall-based space manipulation (WSM) technique that enables users to efficiently select and move distant objects by dynamically squeezing their surrounding space in augmented reality. Users can bring a target object closer by dragging a solid plane behind the object and squeezing the space between them and the plane so that they can select and move the object more delicately and efficiently. We furthermore discuss the unique design challenges of WSM, including the dimension of space reduction and the recognition of the reduced space in relation to the real space. We conducted a user evaluation to verify how WSM improves the performance of the hand-centered object manipulation technique on the HoloLens for moving near objects far away and vice versa. The results indicate that WSM overall performed consistently well and significantly improved efficiency while alleviating arm fatigue.

Charts are commonly used to present data in digital documents such as web pages, research papers, or presentation slides. When the underlying data is not available, it is necessary to extract the data from a chart image to utilize the data for further analysis or improve the chart for more accurate perception. In this paper, we present ChartSense, an interactive chart data extraction system. ChartSense first determines the chart type of a given chart image using a deep learning based classifier, and then extracts underlying data from the chart image using semi-automatic, interactive extraction algorithms optimized for each chart type. To evaluate chart type classification accuracy, we compared ChartSense with ReVision, a system with the state-of-the-art chart type classifier. We found that ChartSense was more accurate than ReVision. In addition, to evaluate data extraction performance, we conducted a user study, comparing ChartSense with WebPlotDigitizer, one of the most effective chart data extraction tools among publicly accessible ones. Our results showed that ChartSense was better than WebPlotDigitizer in terms of task completion time, error rate, and subjective preference.