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 study, 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 are recorded and annotated as participants perform a fixed set of activities. We also test the techniques in an unscripted setting with eight individuals. Motion sensor data are 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 min of a true transition for the scripted data and within 2 min of a true transition on the unscripted data.

中文翻译：

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自动检测活动转变以进行提示

患有认知障碍的人可以从干预策略中受益，例如在记忆笔记本中记录重要信息。然而，训练个人定期使用笔记本需要不断发出提醒。在这项研究中，我们设计和评估基于机器学习的方法，使用数字记忆笔记本界面提供自动提醒。具体来说，我们将活动之间的过渡期确定为发出提示的时间。我们考虑使用监督和无监督机器学习技术来检测活动转换的问题，并发现这两种技术在检测转换期方面都显示出有希望的结果。我们在有脚本的环境中与 15 个人一起测试这些技术。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。我们还在一个没有脚本的环境中与八个人一起测试了这些技术。当参与者进行正常的日常生活时，运动传感器数据就会被记录下来。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，这会导致脚本数据在真实转换后 1 分钟内检测到转换，而无脚本数据在真实转换后 2 分钟内检测到转换。