Real time recognition of human activities from wearable sensors by evolving classifiers
Real time recognition of human activities from wearable sensors by evolving classifiers
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DOI：
10.1109/fuzzy.2011.6007595
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发表时间：
2011-06
期刊：
影响因子：
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通讯作者：
J. Andreu;R. Baruah;P. Angelov
中科院分区：
文献类型：
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作者：
J. Andreu;R. Baruah;P. Angelov
本文提出了一种基于进化自学习模糊规则分类器（eClass）的实时人体活动识别方法。主成分分析（PCA）和线性判别分析（LDA）预处理方法的递归版本与eClass相结合，从而产生了一种新的HAR方法，该方法不需要计算和耗时的预训练以及来自许多主题的数据。所提出的用于进化HAR（eHAR）的新方法考虑了每个用户的具体情况以及她/他的习惯的时间上的可能进化。来自几个可穿戴设备的数据流，这些设备可以开发一种普遍的智能，使它们能够个性化/调整到特定的用户，用于论文的实验部分。
A new approach to real-time human activity recognition (HAR) using evolving self-learning fuzzy rule-based classifier (eClass) will be described in this paper. A recursive version of the principle component analysis (PCA) and linear discriminant analysis (LDA) pre-processing methods is coupled with the eClass leading to a new approach for HAR which does not require computation and time consuming pre-training and data from many subjects. The proposed new method for evolving HAR (eHAR) takes into account the specifics of each user and possible evolution in time of her/his habits. Data streams from several wearable devices which make possible to develop a pervasive intelligence enabling them to personalize/tune to the specific user were used for the experimental part of the paper.