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Efficient out-of-home activity recognition by complementing GPS data with semantic information

机译：通过用语义信息补充GPS数据来进行有效的户外活动识别

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Smartphones have become an indispensable human device due to their increasing functionalities and decreasing prices. Their embedded sensors, including global positioning system (GPS), have opened opportunities to support human activity recognition, both indoor (in assisted living, for instance) and outdoor. This paper proposes a minimalist activity recognition model for out-of-home environments based on a smartphone. The only sensor used is GPS, whose data is enriched with semantic knowledge extracted online from the Internet, and with brief user’s profile data collected off-line. We conducted an experiment for 20 days with 22 subjects in their day to day life, with identification of 13 selected activities, of which three were performed in movement. Experimental results show that the approach has a high activity recognition performance. This demonstrates that an adequate combination of information with different levels of semantic content can produce an efficient non-invasive solution to monitoring human activity in out-of-home environments.
机译：智能手机由于其功能的增加和价格的下降已成为必不可少的人类设备。他们的嵌入式传感器，包括全球定位系统（GPS），为在室内（例如在辅助生活中）和室外的人类活动识别提供了支持的机会。本文提出了一种基于智能手机的户外活动环境的简约活动识别模型。唯一使用的传感器是GPS，GPS的数据丰富了从Internet上在线提取的语义知识以及离线收集的简短用户资料数据。我们对22名受试者的日常生活进行了为期20天的实验，确定了13种选定的活动，其中3项是在运动中进行的。实验结果表明，该方法具有较高的活动识别性能。这表明，具有不同级别语义内容的信息的充分组合可以产生一种有效的非侵入性解决方案，以监视户外环境中的人类活动。

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