基于智能窗口和时空特征分析的人类活动识别

Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments Pub Date : 2016-06-29 DOI:10.1145/2910674.2910697

Fadi Al Machot, H. Mayr

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引用次数: 13

摘要

本文提出了一种有前途的方法来增强智能家居中基于多传感器的活动识别。这项研究起源于积极和辅助生活领域，主要是关于帮助老年人掌握日常生活活动。本文提出了(a)一种可用于在线传感器流的窗口技术和(b)一组不同的统计时空特征来实时识别活动。为了检查整体性能，使用CASAS数据集对该方法进行了测试。结果证明，尽管有大量的类，但基于不同的评估指标仍然具有较高的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Improving Human Activity Recognition by Smart Windowing and Spatio-Temporal Feature Analysis

This paper presents a promising approach to enhance multi-sensor based activity recognition in smart homes. The research is originated in the domain of Active and Assisted Living which mainly is about supporting older people to master their daily life activities. The paper proposes (a) a windowing technique which can be used for online sensor streaming and (b) a set of different statistical spatio-temporal features to recognize activities in real time. In order to check the overall performance, this approach was tested using the CASAS dataset. The results proved a high performance based on different evaluation metrics despite a large number of classes.

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Proceedings of the 9th ACM International Conference on PErvasive Technologies Related to Assistive Environments

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