Automated stress detection using keystroke and linguistic features: An exploratory study
Automated stress detection using keystroke and linguistic features: An exploratory study
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DOI：
10.1016/j.ijhcs.2009.07.005
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发表时间：
2009-10-01
影响因子：
5.4
通讯作者：
Sears, Andrew
中科院分区：
文献类型：
--
作者：
Vizer, Lisa M.;Zhou, Lina;Sears, Andrew
认知和身体功能的监测对于患有或有各种健康状况风险的人的护理至关重要，但现有的解决方案依赖于侵入性方法，不足以进行持续跟踪。随着人口老龄化和寿命的延长，越来越需要有利于更准确和更频繁地监测认知或身体功能状态的侵入性较小的技术。由于使用计算机的老年人数量持续急剧增长，利用正常的日常计算机交互的方法很有吸引力。这项研究探讨了通过监测键盘交互来检测认知和身体压力的可能性，最终目标是检测认知和身体功能的急性或逐渐变化。研究人员已经将个人打字模式的一定程度的变异性和“漂移”归因于情境因素和压力，但这种现象尚未得到充分探索。为了检测与压力相关的打字变化，本研究分析了自发生成文本的击键和语言特征。结果表明，可以根据击键和语言特征对相对于非压力条件的认知和身体压力条件进行分类，其准确率与目前使用情感计算方法获得的准确率相当。所提出的方法很有吸引力，因为它不需要额外的硬件，不引人注目，适合个人用户，并且成本非常低。这项研究证明了利用键盘交互的持续监控来支持早期检测认知和身体功能变化的潜力。 (C) 2009 Elsevier Ltd. 保留所有权利。
Monitoring of cognitive and physical function is central to the care of people with or at risk for various health conditions, but existing solutions rely on intrusive methods that are inadequate for continuous tracking. Less intrusive techniques that facilitate more accurate and frequent monitoring of the status of cognitive or physical function become increasingly desirable as the population ages and lifespan increases. Since the number of seniors using computers continues to grow dramatically, a method that exploits normal daily computer interactions is attractive. This research explores the possibility of detecting cognitive and physical stress by monitoring keyboard interactions with the eventual goal of detecting acute or gradual changes in cognitive and physical function. Researchers have already attributed a certain amount of variability and "drift" in an individual's typing pattern to situational factors as well as stress, but this phenomenon has not been explored adequately. In an attempt to detect changes in typing associated with stress, this research analyzes keystroke and linguistic features of spontaneously generated text. Results show that it is possible to classify cognitive and physical stress conditions relative to non-stress conditions based on keystroke and linguistic features with accuracy rates comparable to those currently obtained using affective computing methods. The proposed approach is attractive because it requires no additional hardware, is unobtrusive, is adaptable to individual users, and is of very low cost. This research demonstrates the potential of exploiting continuous monitoring of keyboard interactions to support the early detection of changes in cognitive and physical function. (C) 2009 Elsevier Ltd. All rights reserved.