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Human activity recognition (HAR) is encountered ina plethora of applications, such as pervasive health care systemsand smart homes. The majority of existing HAR techniquesemploys features extracted from symbolic or frequency-domainrepresentations of the associated data, whilst ignoring completelythe behavior of the underlying data generating dynamical system.To address this problem, this work proposes a novel self-tunedarchitecture for feature extraction and activity recognition bymodelingdoi:10.5281/zenodo.4294527 fatcat:3gekyhhknra2pkfiwlg5lc57k4