The growth in popularity of smart environments has been quite steep in the last decade and so has the demand for smart health assistance systems. A smart home-based prompting system can enhance these technologies to deliver in-home interventions to users for timely reminders or brief instructions describing the way a task should be carried out for successful completion. This technology is in high demand given the desire of people who have physical or cognitive limitations to live independently in their homes. In this paper, with the introduction of the “PUCK” prompting system, we take an approach to automate prompting-based interventions without any predefined rule sets or user feedback. Unlike other approaches, we use simple off-the-shelf sensors and learn the timing for prompts based on real data that are collected with volunteer participants in our smart home test bed. The data mining approaches taken to solve this problem come with the challenge of an imbalanced class distribution that occurs naturally in the data. We propose a variant of an existing sampling technique, SMOTE, to deal with the class imbalance problem. To validate the approach, a comparative analysis with cost-sensitive learning is performed.

中文翻译：

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PUCK：智能环境的自动提示系统：迈向实现自动提示；涉及的挑战。


在过去的十年中，智能环境的普及增长相当迅速，对智能健康辅助系统的需求也是如此。基于智能家居的提示系统可以增强这些技术，向用户提供家庭干预，以便及时提醒或简要说明，描述任务应如何成功完成。鉴于身体或认知受限的人们希望在家中独立生活，这项技术的需求量很大。在本文中，随着“PUCK”提示系统的引入，我们采取了一种方法来自动化基于提示的干预，而无需任何预定义的规则集或用户反馈。与其他方法不同，我们使用简单的现成传感器，并根据智能家居测试台中志愿者参与者收集的真实数据来了解提示的时间。解决此问题所采用的数据挖掘方法面临着数据中自然发生的不平衡类别分布的挑战。我们提出了现有采样技术的变体 SMOTE 来处理类别不平衡问题。为了验证该方法，进行了与成本敏感学习的比较分析。