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• W4386432007 abstract "This paper addresses the problem of Human Activity Recognition (HAR) of people wearing inertial sensors using data from the UCI-HAR dataset. We propose a light residual network which obtains an F1-Score of 97.6% that outperforms previous works, while drastically reducing the number of parameters by a factor of 15, and thus the training complexity. In addition, we propose a new benchmark based on leave-one(person)-out cross-validation to standardize and unify future classifications on the same dataset, and to increase reliability and fairness in the comparisons. In addition, we propose a common bechmark for the UCI-HAR dataset based on Leaving On (Participant) Out Cross-Validation (LOOCV) to justify the robustness of the architecture proposed and providing a fairer approach for comparisons." @default.
• W4386432007 created "2023-09-05" @default.
• W4386432007 creator A5028842593 @default.
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• W4386432007 date "2023-10-01" @default.
• W4386432007 modified "2023-10-11" @default.
• W4386432007 title "Light Residual Network for Human Activity Recognition using Wearable Sensor Data" @default.
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• W4386432007 doi "https://doi.org/10.1109/lsens.2023.3311623" @default.
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