Automated Detection of Activity Transitions for Prompting.
Automated Detection of Activity Transitions for Prompting.
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DOI：
10.1109/thms.2014.2362529
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发表时间：
2015-10
影响因子：
3.6
通讯作者：
Schmitter-Edgecombe M
中科院分区：
文献类型：
--
作者：
Feuz KD;Cook DJ;Rosasco C;Robertson K;Schmitter-Edgecombe M
有认知障碍的人可以从干预策略中受益，比如在记忆笔记本上记录重要信息。然而，培训个人定期使用笔记本电脑需要不断地发送提醒。在这项工作中，我们设计和评估了基于机器学习的方法，用于使用数字存储笔记本界面提供自动提醒。具体地说，我们将活动之间的过渡期确定为发出提示的时间。我们考虑了使用监督和非监督机器学习技术来检测活动转移的问题，发现这两种技术在检测转移周期方面都显示出了良好的结果。我们在15个人的脚本环境中测试了这些技术。当参与者执行一组固定的活动时，运动传感器数据被记录和注释。我们还在8个人的无脚本环境中测试了这些技术。当参与者开始他们的日常生活时，运动传感器数据被记录下来。在脚本化和非脚本化设置中，可以实现大于80%的真阳性率，同时保持低于15%的假阳性率。平均而言，这会导致在脚本数据的真正转换后1分钟内检测到转换，而在非脚本数据的真正转换后的2分钟内检测到转换。
Individuals with cognitive impairment can benefit from intervention strategies like recording important information in a memory notebook. However, training individuals to use the notebook on a regular basis requires a constant delivery of reminders. In this work, we design and evaluate machine learning-based methods for providing automated reminders using a digital memory notebook interface. Specifically, we identify transition periods between activities as times to issue prompts. We consider the problem of detecting activity transitions using supervised and unsupervised machine learning techniques, and find that both techniques show promising results for detecting transition periods. We test the techniques in a scripted setting with 15 individuals. Motion sensors data is recorded and annotated as participants perform a fixed set of activities. We also test the techniques in an unscripted setting with 8 individuals. Motion sensor data is recorded as participants go about their normal daily routine. In both the scripted and unscripted settings a true positive rate of greater than 80% can be achieved while maintaining a false positive rate of less than 15%. On average, this leads to transitions being detected within 1 minute of a true transition for the scripted data and within 2 minutes of a true transition on the unscripted data.