Sensor-based human activity recognition (HAR) is having a significant impact in a wide range of applications in smart city, smart home, and personal healthcare. Such wide deployment of HAR systems often faces the annotation-scarcity challenge; that is, most of the HAR techniques, especially the deep learning techniques, require a large number of training data while annotating sensor data is very time- and effort-consuming. Unsupervised domain adaptation has been successfully applied to tackle this challenge, where the activity knowledge from a well-annotated domain can be transferred to a new, unlabelled domain. However, these existing techniques do not perform well on highly heterogeneous domains. This paper proposes shift-GAN that integrate bidirectional generative adversarial networks (Bi-GAN) and kernel mean matching (KMM) in an innovative way to learn intrinsic, robust feature transfer between two heterogeneous domains. Bi-GAN consists of two GANs that are bound by a cyclic constraint, which enables more effective feature transfer than a classic, single GAN model. KMM is a powerful non-parametric technique to correct covariate shift, which further improves feature space alignment. Through a series of comprehensive, empirical evaluations, shift-GAN has not only achieved its superior performance over 10 state-of-the-art domain adaptation techniques but also demonstrated its effectiveness in learning activity-independent, intrinsic feature mappings between two domains, robustness to sensor noise, and less sensitivity to training data.
Unsupervised Domain Adaptation in Activity Recognition: a GAN-based Approach / Sanabria, Andrea Rosales; Zambonelli, Franco; Ye, Juan. - In: IEEE ACCESS. - ISSN 2169-3536. - (2021), pp. 1-1. [10.1109/ACCESS.2021.3053704]
|Data di pubblicazione:||2021|
|Data di prima pubblicazione:||gen-2021|
|Titolo:||Unsupervised Domain Adaptation in Activity Recognition: a GAN-based Approach|
|Autore/i:||Sanabria, Andrea Rosales; Zambonelli, Franco; Ye, Juan|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/ACCESS.2021.3053704|
|Codice identificativo ISI:||WOS:000619295700001|
|Codice identificativo Scopus:||2-s2.0-85100471007|
|Citazione:||Unsupervised Domain Adaptation in Activity Recognition: a GAN-based Approach / Sanabria, Andrea Rosales; Zambonelli, Franco; Ye, Juan. - In: IEEE ACCESS. - ISSN 2169-3536. - (2021), pp. 1-1. [10.1109/ACCESS.2021.3053704]|
|Tipologia||Articolo su rivista|
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