How We Type: Movement Strategies and Performance in Everyday Typing

Note: updated version of this dataset contains cleaned up typing data where unused (and incorrect) derivative columns were removed. You can derive these from the raw data yourself. ========= This dataset contains motion capture, keylog, eye tracking, and video data of 30 participants, transcribing regular sentences. It is part of the following publication: Anna Maria Feit, Daryl Weir, Antti Oulasvirta. 2016.How We Type: Movement Strategies and Performance in Everyday Typing.In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16). ACM, New York, NY, USA, 4262-4273 The paper revisits the present understanding of typing, which originates mostly from studies of trained typists using the tenfinger touch typing system. Our goal was to characterise the majority of present-day users who are untrained and employ diverse, self-taught techniques. In a transcription task, we compared self-taught typists and those that took a touch typing course. We reported several differences in performance, gaze deployment and movement strategies. The most surprising finding was that self-taught typists can achieve performance levels comparable with touch typists, even when using fewer fingers. Motion capture data exposed 3 predictors of high performance: 1) unambiguous mapping (a letter is consistently pressed by the same finger), 2) active preparation of upcoming keystrokes, and 3) minimal global hand motion. The dataset is free for non-commercial use. Please cite the above work. Note that participants wrote in either Finnish or English.注：本数据集的更新版本已清理输入数据，移除了未使用（且存在错误）的衍生列。您可自行从原始数据中推导这些列。========= 本数据集包含30名参与者在抄写常规语句时的动作捕捉（motion capture）、键盘记录（keylog）、眼动追踪（eye tracking）及视频数据。本数据集源自以下学术成果：Anna Maria Feit、Daryl Weir、Antti Oulasvirta. 2016. 《我们的打字方式：日常打字中的运动策略与表现》. 发表于2016年人机交互系统人为因素会议（CHI '16）. ACM，美国纽约州纽约市，第4262-4273页。该论文重新审视了当前主流的打字认知——其大多源于针对使用十指盲打系统的专业打字员的研究。我们的研究旨在刻画当下绝大多数未经训练、采用多样化自学打字技巧的用户群体。在抄写任务中，我们对比了自学型打字员与接受过盲打课程培训的打字员，并报告了二者在表现、视线部署及运动策略上的多项差异。最令人意外的发现是，即便使用更少手指，自学型打字员仍可达到与盲打打字员相当的表现水平。动作捕捉数据揭示了高性能表现的三项预测因素：1）明确的按键映射（每个字母始终由同一手指按压）；2）对后续按键动作的主动预准备；3）手部全局运动最小化。本数据集可免费用于非商业用途，请引用上述研究成果。请注意，参与者的输入语言为芬兰语或英语。