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首页> 外文期刊>ACM transactions on intelligent systems >COM: A Method for Mining and Monitoring Human Activity Patterns in Home-Based Health Monitoring Systems
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COM: A Method for Mining and Monitoring Human Activity Patterns in Home-Based Health Monitoring Systems

机译：COM：一种基于家庭的健康监视系统中的人类活动模式的挖掘和监视方法

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摘要

The increasing aging population in the coming decades will result in many complications for society and in particular for the healthcare system due to the shortage of healthcare professionals and healthcare facilities. To remedy this problem, researchers have pursued developing remote monitoring systems and assisted living technologies by utilizing recent advances in sensor and networking technology, as well as in the data mining and machine learning fields. In this article, we report on our fully automated approach for discovering and monitoring patterns of daily activities. Discovering and tracking patterns of daily activities can provide unprecedented opportunities for health monitoring and assisted living applications, especially for older adults and individuals with mental disabilities. Previous approaches usually rely on preselected activities or labeled data to track and monitor daily activities. In this article, we present a fully automated approach by discovering natural activity patterns and their variations in real-life data. We will show how our activity discovery component can be integrated with an activity recognition component to track and monitor various daily activity patterns. We also provide an activity visualization component to allow caregivers to visually observe and examine the activity patterns using a user-friendly interface. We validate our algorithms using real-life data obtained from two apartments during a three-month period.
机译：在未来几十年中，随着人口老龄化的增加，由于医疗保健专业人员和医疗机构的短缺，将给社会，尤其是医疗保健系统带来许多麻烦。为了解决这个问题，研究人员一直致力于利用传感器和网络技术以及数据挖掘和机器学习领域的最新进展来开发远程监控系统和辅助生活技术。在本文中，我们报告了用于发现和监视日常活动模式的全自动方法。发现和跟踪日常活动的方式可以为健康监测和辅助生活应用提供空前的机会，尤其是对于老年人和智障人士。先前的方法通常依赖于预先选择的活动或标记的数据来跟踪和监视日常活动。在本文中，我们通过发现自然活动模式及其在现实生活数据中的变化，提出了一种全自动方法。我们将展示如何将我们的活动发现组件与活动识别组件集成在一起，以跟踪和监视各种日常活动模式。我们还提供了一个活动可视化组件，以允许护理人员使用用户友好的界面直观地观察和检查活动模式。我们使用三个月内从两个公寓获得的真实数据来验证算法。

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