Abstract: Long-term continuous use of computers would bring negative effects on users' health. In order to detect users fatigue level in a non-invasive manner, an approach that is able to measure fatigue level on hand muscle based on the keyboard and mouse events was proposed. The proposed method integrated keying action match, data noise filtering, and feature vector extraction/classification together to collect and analyze the delay characteristics of both keying and hitting actions, upon which the detection of fatigue level on hand muscle could be enabled. With the detected fatigue level, friends belonging to the same virtual community on current social networks could be, in real-time, alerted and persuaded to take a health-conscious way in their daily use of computers. Particularly, an interesting conclusion has been made that there is an obvious negative correlation between keying (hitting) delay and fatigue level of hand muscle. The experimental validation conducted on two-week data collected from 15 participants shows that the proposed method is effective in detecting users fatigue level and distributing fatigue-related health information on social network platform.

Key words: fatigue evaluation, keyboard and mouse event, social network, health promotion, random forest

摘要： 长时间持续使用电脑会对人体造成健康危害,针对目前尚无非入侵式电脑使用疲劳度检测的有效方法的现状,提出了一种基于键盘和鼠标事件实时监测的非干扰式手部肌肉疲劳度评估方法。该方法经过按键动作匹配、数据去噪、特征向量提取、分类等处理,分析一段时间内两类按键的时延特性,实现对手部肌肉疲劳程度的评估和监测。利用社交网络,将检测的疲劳状态与好友进行分享,以好友劝导、健康激励的方式促使用户逐渐改变不健康的电脑使用习惯。该方法在15位用户中进行了为期2周的实验,结果验证了所提方法对疲劳度评估的有效性,以及在社交网络平台分享相关健康信息的可行性,并发现按键延迟与手部肌肉疲劳程度成负相关关系。

关键词: 疲劳评估, 键盘鼠标事件, 社交网络, 健康促进, 随机森林