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In Shift and In Variance: Assessing the Robustness of HAR Deep Learning Models Against Variability

Author(s): Khaked AA; Oishi N; Roggen D; Lago P;

Deep learning (DL)-based Human Activity Recognition (HAR) using wearable inertial measurement unit (IMU) sensors can revolutionize continuous health monitoring and early disease prediction. However, most DL HAR models are untested in their robustness to real-world variability, as they are trained on limited lab-controlled data. In this study, we isolated ...

Article GUID: 39860799


Robustness and evolvability: Revisited, redefined and applied

Author(s): Kharma N; Bédard-Couture R;

Building on and extending existing definitions of robustness and evolvability, we propose and utilize new formal definitions, with matching measures, of robustness and evolvability of systems with genotypes and corresponding phenotypes. We explain and show how these measures are more general and more representative of the concepts they stand for, than the ...

Article GUID: 39098381


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