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Recognition of human activities for wellness management using a smartphone and a smartwatch: A boosting approach

机译：使用智能手机和Smartwatch对健康管理的人类活动的认识：提升方法

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摘要

Mobile health applications are considered to be powerful tools for activity-based wellness management. With the availability of multimodal sensors in smart devices used in our daily lives, it is possible to track human activity and deliver context-aware wellness services. The embedded sensors in naturally used devices such as smartphones, smartwatches, and wearables contain rich information that can be integrated for human activity recognition. Our research demonstrates how powerful boosting algorithms can extract knowledge for human activity classification in a real-life setting. Our results show that boosting classifiers outperform traditional machine learning classifiers in the detection of basic human activities such as walking, standing, sitting, exercise, and sleeping. Further, we perform feature engineering to compare the potential of a smartphone and a smartwatch in activity detection. Our feature engineering strategy provides directions about the selection of sensor features for improvement in classification of basic human activities. The theoretical and practical implications of this research for activity-based wellness management are also discussed.
机译：移动运行效果被认为是基于活动的健康管理的强大工具。随着我们日常生活中使用的智能设备中的多模式传感器的可用性，可以跟踪人类活动并提供背景感知健康服务。自然使用的设备中的嵌入式传感器，如智能手机，智能手表和可穿戴设备，包含丰富的信息，可以融入人类活动识别。我们的研究演示了如何强大的提升算法可以在现实生活环境中提取对人类活动分类的知识。我们的结果表明，促进分类器优于传统的机器学习分类器，在检测的基本人类活动中，如行走，站立，坐着，运动和睡眠等。此外，我们执行特征工程以比较智能手机的潜力和活动检测中的智能手表。我们的特色工程策略提供了关于选择性的传感器特征，以改善基本人类活动的分类。还讨论了该研究基于活动的健康管理的理论和实践意义。

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