Related papers: Learning with Category-Equivariant Architectures for Human Activity Recognition

Learning with Category-Equivariant Architectures for Human Activity Recognition

URL: http://arxiv.org/abs/2511.01139v2
Date: Tue, 04 Nov 2025 02:33:12 GMT
Title: Learning with Category-Equivariant Architectures for Human Activity Recognition
Authors: Yoshihiro Maruyama,
Abstract summary: We propose CatEquiv, a category-equivariant neural network for Human Activity Recognition (HAR) from inertial sensors.<n>We introduce a symmetry category that jointly represents cyclic time shifts, positive gain scalings, and the sensor-hierarchy poset, capturing the categorical symmetry structure of the data.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We propose CatEquiv, a category-equivariant neural network for Human Activity Recognition (HAR) from inertial sensors that systematically encodes temporal, amplitude, and structural symmetries. We introduce a symmetry category that jointly represents cyclic time shifts, positive gain scalings, and the sensor-hierarchy poset, capturing the categorical symmetry structure of the data. CatEquiv achieves equivariance with respect to the categorical symmetry product. On UCI-HAR under out-of-distribution perturbations, CatEquiv attains markedly higher robustness compared with circularly padded CNNs and plain CNNs. These results demonstrate that enforcing categorical symmetries yields strong invariance and generalization without additional model capacity.

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