Guided-GAN: Adversarial Representation Learning for Activity Recognition with Wearables
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by
Alireza Abedin, Hamid Rezatofighi, Damith C. Ranasinghe
2021
Abstract
Human activity recognition (HAR) is an important research field in ubiquitous
computing where the acquisition of large-scale labeled sensor data is tedious,
labor-intensive and time consuming. State-of-the-art unsupervised remedies
investigated to alleviate the burdens of data annotations in HAR mainly explore
training autoencoder frameworks. In this paper: we explore generative
adversarial network (GAN) paradigms to learn unsupervised feature
representations from wearable sensor data; and design a new GAN
framework-Geometrically-Guided GAN or Guided-GAN-for the task. To demonstrate
the effectiveness of our formulation, we evaluate the features learned by
Guided-GAN in an unsupervised manner on three downstream classification
benchmarks. Our results demonstrate Guided-GAN to outperform existing
unsupervised approaches whilst closely approaching the performance with fully
supervised learned representations. The proposed approach paves the way to
bridge the gap between unsupervised and supervised human activity recognition
whilst helping to reduce the cost of human data annotation tasks.
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