Document Type

Conference Paper

Rights

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

Disciplines

Computer Sciences, Robotics and automatic control, Automation and control systems

Publication Details

Appeared in the Proceedings of the 34th International ECMS Conference on Modelling and Simulation, ECMS 2020, pages 183--189

Abstract

We propose a new approach to activity discovery, based on the neural language modelling of streaming sensor events. Our approach proceeds in multiple stages: we build binary links between activities using probability distributions generated by a neural language model trained on the dataset, and combine the binary links to produce complex activities. We then use the activities as sensor events, allowing us to build complex hierarchies of activities. We put an emphasis on dealing with interleaving, which represents a major challenge for many existing activity discovery systems. The system is tested on a realistic dataset, demonstrating it as a promising solution to the activity discovery problem.

DOI

https://doi.org/10.7148/2020-0183


Share

COinS