Lifelogging has become a prominent research topic in recent years. Wearable sensors like Fitbits and smart watches are now increasingly popular for recording ones activities. Some researchers are also exploring keystroke dynamics for lifelogging. Keystroke dynamics refers to the process of measuring and assessing a persons typing rhythm on digital devices. A digital footprint is created when a user interacts with devices like keyboards, mobile phones or touch screen panels and the timing of the keystrokes is unique to each individual though likely to be affected by factors such as fatigue, distraction or emotional stress. In this work we explore the relationship between keystroke dynamics as measured by the timing for the top-10 most frequently occurring bi-grams in English, and the emotional state and stress of an individual as measured by heart rate variability (HRV). We collected keystroke data using the Loggerman application while HRV was simultaneously gathered. With this data we performed an analysis to determine the relationship between variations in keystroke dynamics and variations in HRV. Our conclusion is that we need to use a more detailed representation of keystroke timing than the top-10 bigrams, probably personalised to each user.
翻译:近年来,生活博客已成为一个突出的研究课题。像Fitbits和智能手表这样的穿戴感应器在记录这些活动方面越来越受欢迎。一些研究人员也在探索生命博客的键盘动态。键盘动态是指测量和评估在数字设备上打字节奏的人的过程。当用户与键盘、移动电话或触摸屏幕面板等设备发生互动时,即产生数字足迹,键盘的时间安排对每个人来说是独一无二的,尽管可能受到疲劳、分散或情绪压力等因素的影响。在这项工作中,我们需要探索键盘动态之间的关系,即以最经常发生的10大英字节的时机来衡量,以及以心率变化衡量的个人情绪状态和压力(HRV)。我们在同时收集HRV时,利用Logggerman应用程序收集了键盘点数数据。我们用这些数据进行了分析,以确定键盘动态变化与HRV变化之间的关系。我们的结论是,我们需要使用比前10大字框(可能针对每个用户的个人)更详细的键盘时间代表。