Real-time human activity recognition from wireless sensors using evolving fuzzy systems
Real-time human activity recognition from wireless sensors using evolving fuzzy systems
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DOI：
10.1109/fuzzy.2010.5584280
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发表时间：
2010-07
期刊：
影响因子：
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通讯作者：
J. Andreu;P. Angelov
中科院分区：
文献类型：
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作者：
J. Andreu;P. Angelov
提出了一种基于自学习进化模糊规则分类器的可穿戴无线加速度传感器流数据实时知识提取方法。基于对真实的受试者的实验，我们收集了18种不同分类广告活动的数据。在对数据进行预处理和分类后，根据活动的时间顺序，我们在识别活动序列时达到了99.81%的准确率。只要应用程序在可穿戴智能/智能传感器上运行，该技术就允许重新训练系统，从而在整个时间内获得更好的分类率，而不会增加性能延迟。
A new approach to real-time knowledge extraction from streaming data generated by wearable wireless accelerometers based on self-learning evolving fuzzy rule-based classifier is proposed and evaluated in this paper. Based on experiments with real subjects we collected data from 18 different classifieds activities. After preprocessing and classifying data depending on the sequence of activities regarding time, we achieved up to 99.81% of accuracy in recognizing a sequence of activities. This technique allows re-training the system as long as the application is running on the wearable intelligent/smart sensor, getting a better classification rate throughout the time without an increase of the delay in performance.