Mouse-tracking of computer system users represents a less expensive, but also a far more applicable alternative to eye-tracking. The main disadvantage of mouse-tracking are errors manifested as discrepancies between the actual eye-gaze position and the mouse cursor position. This paper presents a method for automated correction of errors arising in mouse-tracking. Our approach draws upon information theory and employs Shannon entropy. The method is based on calculating the entropy of a visual representation of a Web page, i.e., we quantify information potential values of various positions. Information obtained, thereby, is paired with cumulative time intervals, spent by the mouse cursor in each position. In this way, we identify cursor positions that do not match eye-gaze positions. To verify the effectiveness of our method, we compared the eye gaze and mouse cursor heat maps in the following ways: We calculated the coefficient of correlation between the two; we computed Euclidean distance between their centers of gravity; and we performed visual comparison.
翻译:计算机系统用户的鼠标跟踪是一种成本较低,但也是比眼睛跟踪更适用的替代方法。鼠标跟踪的主要缺点是实际视网膜位置与鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标鼠标跟踪位置之间的差异。本文介绍了自动纠正鼠标跟踪过程中出现的错误的一种方法。我们的方法是利用信息理论并使用Shannon entropy。该方法的基础是计算网页视觉显示的英特比,即我们量化了不同位置的信息潜在值。因此,所获得的信息与鼠标鼠标鼠标鼠标在每个位置上花费的累积时间间隔相匹配。我们通过这种方式确定与眼视网格位置不匹配的光标位置。为了验证我们的方法的有效性,我们用以下方式比较了眼视网眼和鼠标鼠标鼠标光标光标光标光标光标的热图:我们计算了两者之间的关联系数;我们计算了两者的埃克利德南方点之间的距离;我们计算了它们重点之间的距离;我们进行了视觉比较。