Document Type

Conference Paper

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Computer Sciences, Automation and control systems

Publication Details

Presented with a poster at the 4th Irish Conference on Artificial Intelligence and Cognitive Science in UCD, Dublin in September 2016. Appears in the proceedings for that conference.

Abstract

Activity discovery is a challenging machine learning problem where we seek to uncover new or altered behavioural patterns in sensor data. In this paper we motivate and introduce a novel approach to activity discovery based on modern deep learning techniques. We hypothesise that our proposed approach can deal with interleaved datasets in a more intelligent manner than most existing AD methods. We also build upon prior work building hierarchies of activities that capture the inherent ag- gregate nature of complex activities and show how this could plausibly be adapted to work with the deep learning technique we present. Finally, we briefly talk about the challenge of evaluating activity discovery systems in a fair way and outline our future plans for implementing this model.

DOI

https://doi.org/10.21427/j899-0254

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

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