On recognizing abnormal human behaviours by data stream mining with misclassified recalls
Fong S.1; Hu S.1; Song W.2; Cho K.5; Wong R.K.3; Mohammed S.4
Source Publication26th International World Wide Web Conference 2017, WWW 2017 Companion
AbstractHuman activity recognition (HAR) has been a popular research topic, because of its importance in security and healthcare contributing to aging societies. One of the emerging applications of HAR is to monitor needy people such as elders, patients of disabled, or undergoing physical rehabilitation, using sensing technology. In this paper, an improved version of Very Fast Decision Tree (VFDT) is proposed which makes use of misclassified results for post-learning. Specifically, a new technique namely Misclassified Recall (MR) which is a post-processing step for relearning a new concept, is formulated. In HAR, most misclassified instances are those belonging to ambiguous movements. For examples, squatting involves actions in between standing and sitting, falling straight down is a sequence of standing, possibly body tiling or curling, bending legs, squatting and crashing down on the floor; and there may be totally new (unseen) actions beyond the training instances when it comes to classifying “abnormal” human behaviours. Think about the extreme postures of how a person collapses and free falling from height. Experiments using wearable sensing data for multi-class HAR is used, to test the efficacy of the new methodology VFDT+MR, in comparison to a classical data stream mining algorithm VFDT alone.
KeywordClassification Data stream mining Human activity recognition
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Universidade de Macau
2.North China University of Technology
3.University of New South Wales (UNSW) Australia
4.Lakehead University
5.Dongguk University, Seoul
Recommended Citation
GB/T 7714
Fong S.,Hu S.,Song W.,et al. On recognizing abnormal human behaviours by data stream mining with misclassified recalls[C],2019:1129-1135.
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