AAAI Publications, 2017 AAAI Fall Symposium Series

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Does the Human's Representation Matter for Unsupervised Activity Recognition?
Richard G. Freedman, Shlomo Zilberstein

Last modified: 2017-10-09

Abstract


Unsupervised activity recognition introduces the opportunity for more robust interaction experiences with machines because the human is not limited to only acting with respect to a training dataset. Many approaches currently use latent variable models that have been well studied and developed by the natural language research communities. However, these models are simply used as-is or with minor tweaks on datasets that present an analogy between sensor reading sequences and text documents. Although words have well-defined semantics so that the learned clusters can be interpreted and verified, this is not often the case for sensor readings. For example, novel data from new human activities need to be classified, which relies on the learned clusters; so how does one confirm that new activities are being correctly processed by a robot for interaction? We present several ways that motion capture information can be represented for use in these methods, and then illustrate how the representation choice has the potential to produce variations in the learned clusters.

Keywords


Sensor Data Representation; Latent Semantic Analysis; Topic Model; Activity Recognition

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