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Discovery and recognition of emerging human activities using a hierarchical mixture of directional statistical models

Research output: Contribution to journalArticle

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Abstract

Human activity recognition plays a significant role in enabling pervasive applications as it abstracts low-level noisy sensor data into high-level human activities, which applications can respond to. With more and more activity-aware applications deployed in real-world environments, a research challenge emerges – discovering and learning new activities that have not been pre-defined or observed in the training phase. This paper tackles this challenge by proposing a hierarchical mixture of directional statistical models.The model supports incrementally, continuously updating the activity model over time with the reduced annotation effort and without the need for storing historical sensor data. We have validated this solution on four publicly available, third-party smart home datasets,and have demonstrated up to 91.5% accuracies of detecting and recognising new activities.
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Original languageEnglish
JournalIEEE Transactions on Knowledge and Data Engineering
VolumeEarly Access
Early online date15 Mar 2019
DOIs
Publication statusE-pub ahead of print - 15 Mar 2019

    Research areas

  • Data models, Activity recognition, Training, Training data, Kernel, Mixture models, Smart homes

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