Discovery and recognition of emerging human activities using a hierarchical mixture of directional statistical models
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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.
Fang , L , Ye , J & Dobson , S A 2019 , ' Discovery and recognition of emerging human activities using a hierarchical mixture of directional statistical models ' , IEEE Transactions on Knowledge and Data Engineering , vol. Early Access . https://doi.org/10.1109/TKDE.2019.2905207
IEEE Transactions on Knowledge and Data Engineering
© 2019, IEEE. This work has been made available online in accordance with the publisher's policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/TKDE.2019.2905207
DescriptionFunding: UK EPSRC under grant number EP/N007565/1, “Science of Sensor Systems Software”.
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